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What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning
 
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What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning - ANOMALY DETECTION definition - ANOMALY DETECTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In data mining, anomaly detection (also outlier detection) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.[1] Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.[2] In particular in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular unsupervised methods) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro clusters formed by these patterns.[3] Three broad categories of anomaly detection techniques exist.[1] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then testing the likelihood of a test instance to be generated by the learnt model.
Views: 5867 The Audiopedia
Build an Antivirus in 5 Min - Fresh Machine Learning #7
 
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In this video, we talk about how machine learning is used to create antivirus programs! Specifically, a classifier can be trained to detect whether or not some piece of software is malicious. Check out my friend Danooct1's Youtube channel on viruses (dope AF): https://www.youtube.com/user/danooct1 The code in the video is here: https://github.com/llSourcell/antivirus_demo I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Paper 1: A Machine Learning Approach to Anomaly based detection on Android https://arxiv.org/pdf/1512.04122.pdf Paper 2: SMARTBot - A Behavior Detection Framework for Botnets http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792466/ Paper 3: A New Malware Detection Approach Using Bayesian Classification https://arxiv.org/pdf/1608.00848v1.pdf More on Machine Learning + Cybersecurity: http://www.lancaster.ac.uk/pg/richarc2/dissertation.pdf https://www.sec.in.tum.de/malware-detection-ws0910/ https://insights.sei.cmu.edu/sei_blog/2011/09/using-machine-learning-to-detect-malware-similarity.html I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now. I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 103748 Siraj Raval
2007-02-28 CERIAS - Assured Information Sharing between Trustworthy, Semi-trustworthy and Untrust...
 
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Recorded: 02/28/2007 CERIAS Security Seminar at Purdue University Assured Information Sharing between Trustworthy, Semi-trustworthy and Untrust... Bhavani Thuraisingham, The University of Texas at Dallas Data mining is the process of posing queries and extracting patterns, often previously unknown from large quantities of data using pattern matching or other reasoning techniques. Data mining has many ap-plications in security including for national security as well as for cyber security. The threats to national security include attacking buildings, destroying critical infrastructures such as power grids and telecom-munication systems. Data mining techniques are being investigated to find out who the suspicious people are and who is capable of carrying out terrorist activities. Cyber security is involved with protecting the computer and network systems against corruption due to Trojan horses, worms and viruses. Data mining is also being applied to provide solutions such as intrusion detection and auditing. The first part of the presentation will discuss my joint research with Prof. Latifur Khan and our students at the University of Texas at Dallas on data mining for cyber security applications For example; anomaly detection techniques could be used to detect unusual patterns and behaviors. Link analysis may be used to trace the viruses to the perpetrators. Classification may be used to group various cyber attacks and then use the profiles to detect an attack when it occurs. Prediction may be used to determine potential future attacks depending in a way on information learnt about terrorists through email and phone conversations. Data mining is also being applied for intrusion detection and auditing. Other applications include data mining for malicious code detection such as worm detection and managing firewall policies.This second part of the presentation will discuss the various types of threats to national security and de-scribe data mining techniques for handling such threats. Threats include non real-time threats and real-time threats. We need to understand the types of threats and also gather good data to carry out mining and obtain useful results. The challenge is to reduce false positives and false negatives. The third part of the presentation will discuss some of the research challenges. We need some form of real-time data mining, that is, the results have to be generated in real-time, we also need to build models in real-time for real-time intrusion detection. Data mining is also being applied for credit card fraud de-tection and biometrics related applications. While some progress has been made on topics such as stream data mining, there is still a lot of work to be done here. Another challenge is to mine multimedia data including surveillance video. Finally, we need to maintain the privacy of individuals. Much research has been carried out on privacy preserving data mining. In summary, the presentation will provide an overview of data mining, the various types of threats and then discuss the applications of data mining for malicious code detection and cyber security. Then we will discuss the consequences to privacy. Dr. Bhavani Thuraisingham joined The University of Texas at Dallas in October 2004 as a Professor of Computer Science and Director of the Cyber Security Research Center in the Erik Jonsson School of Engineering and Computer Science. She is an elected Fellow of three professional organizations: the IEEE (Institute for Electrical and Electronics Engineers), the AAAS (American Association for the Advancement of Science) and the BCS (British Computer Society) for her work in data security. She received the IEEE Computer Society�s prestigious 1997 Technical Achievement Award for �outstanding and innovative contributions to secure data management.�Dr Thuraisingham�s work in information security and information management has resulted in over 70 journal articles, over 200 refereed conference papers and workshops, and three US patents. She is the au-thor of seven books in data management, data mining and data security including one on data mining for counter-terrorism and another on Database and Applications Security and is completing her eighth book on Trustworthy Semantic Web. She has given over 30 keynote presentations at various technical confer-ences and has also given invited talks at the White House Office of Science and Technology Policy and at the United Nations on Data Mining for counter-terrorism. She serves (or has served) on editorial boards of leading research and industry journals and currently serves as the Editor in Chief of Computer Stan-dards and Interfaces Journal. She is also an Instructor at AFCEA�s (Armed Forces Communications and Electronics Association) Professional Development Center and has served on panels for the Air For... (Visit: www.cerias.purude.edu)
Views: 166 ceriaspurdue
intrusion detection projects
 
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Views: 112 PHD Projects
What is SYNTHETIC DATA? What does SYNTHETIC DATA mean? SYNTHETIC DATA meaning & explanation
 
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What is SYNTHETIC DATA? What does SYNTHETIC DATA mean? SYNTHETIC DATA meaning - SYNTHETIC DATA definition - SYNTHETIC DATA explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Synthetic data is "any production data applicable to a given situation that are not obtained by direct measurement" according to the McGraw-Hill Dictionary of Scientific and Technical Terms; where Craig S. Mullins, an expert in data management, defines production data as "information that is persistently stored and used by professionals to conduct business processes.". The creation of synthetic data is an involved process of data anonymization; that is to say that synthetic data is a subset of anonymized data. Synthetic data is used in a variety of fields as a filter for information that would otherwise compromise the confidentiality of particular aspects of the data. Many times the particular aspects come about in the form of human information (i.e. name, home address, IP address, telephone number, social security number, credit card number, etc.). Synthetic data are generated to meet specific needs or certain conditions that may not be found in the original, real data. This can be useful when designing any type of system because the synthetic data are used as a simulation or as a theoretical value, situation, etc. This allows us to take into account unexpected results and have a basic solution or remedy, if the results prove to be unsatisfactory. Synthetic data are often generated to represent the authentic data and allows a baseline to be set. Another use of synthetic data is to protect privacy and confidentiality of authentic data. As stated previously, synthetic data is used in testing and creating many different types of systems; below is a quote from the abstract of an article that describes a software that generates synthetic data for testing fraud detection systems that further explains its use and importance. "This enables us to create realistic behavior profiles for users and attackers. The data is used to train the fraud detection system itself, thus creating the necessary adaptation of the system to a specific environment." Synthetic data are used in the process of data mining. Testing and training fraud detection systems, confidentiality systems and any type of system is devised using synthetic data. As described previously, synthetic data may seem as just a compilation of “made up” data, but there are specific algorithms and generators that are designed to create realistic data. This synthetic data assists in teaching a system how to react to certain situations or criteria. Researcher doing clinical trials or any other research may generate synthetic data to aid in creating a baseline for future studies and testing. For example, intrusion detection software is tested using synthetic data. This data is a representation of the authentic data and may include intrusion instances that are not found in the authentic data. The synthetic data allows the software to recognize these situations and react accordingly. If synthetic data was not used, the software would only be trained to react to the situations provided by the authentic data and it may not recognize another type of intrusion. Synthetic data is also used to protect the privacy and confidentiality of a set of data. Real data contains personal/private/confidential information that a programmer, software creator or research project may not want to be disclosed. Synthetic data holds no personal information and cannot be traced back to any individual; therefore, the use of synthetic data reduces confidentiality and privacy issues.....
Views: 995 The Audiopedia
What is DEEP PACKET INSPECTION? What does DEEP PACKET INSPECTION mean?
 
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What is DEEP PACKET INSPECTION? What does DEEP PACKET INSPECTION mean? DEEP PACKET INSPECTION meaning - DEEP PACKET INSPECTION definition - DEEP PACKET INSPECTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Deep packet inspection (DPI, also called complete packet inspection and information extraction or IX) is a form of computer network packet filtering that examines the data part (and possibly also the header) of a packet as it passes an inspection point, searching for protocol non-compliance, viruses, spam, intrusions, or defined criteria to decide whether the packet may pass or if it needs to be routed to a different destination, or, for the purpose of collecting statistical information that functions at the Application layer of the OSI (Open Systems Interconnection model). There are multiple headers for IP packets; network equipment only needs to use the first of these (the IP header) for normal operation, but use of the second header (such as TCP or UDP) is normally considered to be shallow packet inspection (usually called stateful packet inspection) despite this definition. There are multiple ways to acquire packets for deep packet inspection. Using port mirroring (sometimes called Span Port) is a very common way, as well as an optical splitter. Deep Packet Inspection (and filtering) enables advanced network management, user service, and security functions as well as internet data mining, eavesdropping, and internet censorship. Although DPI technology has been used for Internet management for many years, some advocates of net neutrality fear that the technology may be used anticompetitively or to reduce the openness of the Internet. DPI is used in a wide range of applications, at the so-called "enterprise" level (corporations and larger institutions), in telecommunications service providers, and in governments. DPI combines the functionality of an intrusion detection system (IDS) and an Intrusion prevention system (IPS) with a traditional stateful firewall. This combination makes it possible to detect certain attacks that neither the IDS/IPS nor the stateful firewall can catch on their own. Stateful firewalls, while able to see the beginning and end of a packet flow, cannot catch events on their own that would be out of bounds for a particular application. While IDSs are able to detect intrusions, they have very little capability in blocking such an attack. DPIs are used to prevent attacks from viruses and worms at wire speeds. More specifically, DPI can be effective against buffer overflow attacks, denial-of-service attacks (DoS), sophisticated intrusions, and a small percentage of worms that fit within a single packet. DPI-enabled devices have the ability to look at Layer 2 and beyond Layer 3 of the OSI model. In some cases, DPI can be invoked to look through Layer 2-7 of the OSI model. This includes headers and data protocol structures as well as the payload of the message. DPI functionality is invoked when a device looks or takes other action, based on information beyond Layer 3 of the OSI model. DPI can identify and classify traffic based on a signature database that includes information extracted from the data part of a packet, allowing finer control than classification based only on header information. End points can utilize encryption and obfuscation techniques to evade DPI actions in many cases. A classified packet may be redirected, marked/tagged (see quality of service), blocked, rate limited, and of course, reported to a reporting agent in the network. In this way, HTTP errors of different classifications may be identified and forwarded for analysis. Many DPI devices can identify packet flows (rather than packet-by-packet analysis), allowing control actions based on accumulated flow information. ...
Views: 2552 The Audiopedia
Intrusion detection system
 
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An intrusion detection system (IDS) is a device or software application that monitors network or system activities for malicious activities or policy violations and produces reports to a management station. IDS come in a variety of “flavors” and approach the goal of detecting suspicious traffic in different ways. There are network based (NIDS) and host based (HIDS) intrusion detection systems. Some systems may attempt to stop an intrusion attempt but this is neither required nor expected of a monitoring system. Intrusion detection and prevention systems (IDPS) are primarily focused on identifying possible incidents, logging information about them, and reporting attempts. In addition, organizations use IDPSes for other purposes, such as identifying problems with security policies, documenting existing threats and deterring individuals from violating security policies. IDPSes have become a necessary addition to the security infrastructure of nearly every organization. IDPSes typically record information related to observed events, notify security administrators of important observed events and produce reports. Many IDPSes can also respond to a detected threat by attempting to prevent it from succeeding. They use several response techniques, which involve the IDPS stopping the attack itself, changing the security environment (e.g. reconfiguring a firewall) or changing the attack's content. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1596 Audiopedia
Predicting Bugs by Analyzing Software History
 
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Almost all software contains undiscovered bugs, ones that have not yet been exposed by testing or by users. What is the location of these bugs? This talk presents two approaches for predicting the location of bugs by analyzing software history. First, the bug cache contains 10 temporal, spatial, changed-entity, and new-entity locality. After processing, files in the bug cache contain 73-95 likelihood).
Views: 261 Microsoft Research
Data loss prevention software
 
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Data loss/leak prevention solution is a system that is designed to detect potential data breach / data ex-filtration transmissions and prevent them by monitoring, detecting and blocking sensitive data while in-use (endpoint actions), in-motion (network traffic), and at-rest (data storage). In data leakage incidents, sensitive data is disclosed to unauthorized personnel either by malicious intent or inadvertent mistake. Such sensitive data can come in the form of private or company information, intellectual property (IP), financial or patient information, credit-card data, and other information depending on the business and the industry. The terms "data loss" and "data leak" are closely related and are often used interchangeably, though they are somewhat different. Data loss incidents turn into data leak incidents in cases where media containing sensitive information is lost and subsequently acquired by unauthorized party. However, a data leak is possible without the data being lost in the originating side. Some other terms associated with data leakage prevention are information leak detection and prevention (ILDP), information leak prevention (ILP), content monitoring and filtering (CMF), information protection and control (IPC), and extrusion prevention system (EPS), as opposed to intrusion prevention system. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 153 Audiopedia
Final Year Projects | Using Rule Ontology in Repeated Rule Acquisition from Similar Web Sites
 
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Final Year Projects | Using Rule Ontology in Repeated Rule Acquisition from Similar Web Sites More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 312 Clickmyproject
12 Signs Your Computer Has Been Hacked
 
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There are important signs your computer has been hacked and your data has been stolen. What should you do? Learn the easiest ways to protect your computer from hackers. Cyber attacks have recently become a very popular problem, so everyone is worried about protecting their data. To prevent your passwords or other important data from being stolen, you have to pay attention to any changes on your computer. TIMESTAMPS The antivirus is switched off 0:49 You receive fake antivirus warnings 1:29 Your passwords don’t work 2:05 Your number of friends has grown 2:59 New icons appear on your dashboard 3:25 The cursor moves on its own 3:50 Your printer doesn’t work properly 4:30 You are redirected to different websites 5:02 Your files are deleted by someone else 5:36 Your data is on the internet, even though you didn’t put it there 6:06 There is unusual webcam behavior 6:56 Your computer works very slowly 7:28 What you should do 8:19 SUMMARY What you should do: • Warn your friends and other people to whom you sent emails that your computer has been hacked. Tell them not to open messages from you and not to click on any links from you. • Tell your bank about a possible leak of your data. Find out how to protect your money. • Delete all unfamiliar programs and also those you can’t launch. • Install a reliable antivirus, and scan your system. Some companies make trial versions. • Change the passwords on all your accounts. • If you still feel that the problem is not solved, ask a specialist. Subscribe to Bright Side : https://goo.gl/rQTJZz ---------------------------------------------------------------------------------------- Our Social Media: Facebook: https://www.facebook.com/brightside/ Instagram: https://www.instagram.com/brightgram/ 5-Minute Crafts Youtube: https://www.goo.gl/8JVmuC  ---------------------------------------------------------------------------------------- For more videos and articles visit: http://www.brightside.me/
Views: 2029232 BRIGHT SIDE
Kim Komando Flash Tip: These forensic tools expose any photo's hidden data
 
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To expose any photo’s hidden data, forensic tools may be used. Forensic tools can detect simple metadata or perform more advanced functions, such as to determine if a photo was altered. One of the ways to retrieve hidden image data is by looking at its EXIF file. Read the full article here: https://bit.ly/2pzllDR Subscribe to Kim’s channel: http://bit.ly/2nJAL79 ABOUT KIM KOMANDO Kim Komando – "America's Digital Goddess" – is one of America's most successful radio hosts and Web entrepreneurs - and a trusted guide to millions through the thickets of today's digital lifestyle. Get the latest tech news: https://www.komando.com Check out my free podcasts: https://bit.ly/2jwXFNY Join the conversation on Facebook: http://bit.ly/1QcFQzE Follow Kim on Twitter: http://bit.ly/2nKmfMs Visit the Komando Shop: http://bit.ly/2FNFn3c
International Journal on Cryptography and Information Security ( IJCIS)
 
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Scope & Topics International Journal on Cryptography and Information Security ( IJCIS) is an open access peer reviewed journal that focuses on cutting-edge results in applied cryptography and Information security. It aims to bring together scientists, researchers and students to exchange novel ideas and results in all aspects of cryptography, coding and Information security. Topics of interest include but are not limited to, the following Cryptographic protocols Cryptography and Coding Untraceability Privacy and authentication Key management Authentication Trust Management Quantum cryptography Computational Intelligence in Security Artificial Immune Systems Biological & Evolutionary Computation Intelligent Agents and Systems Reinforcement & Unsupervised Learning Autonomy-Oriented Computing Coevolutionary Algorithms Fuzzy Systems Biometric Security Trust models and metrics Regulation and Trust Mechanisms Data Integrity Models for Authentication, Trust and Authorization Wireless Network Security Information Hiding Data & System Integrity E- Commerce Access Control and Intrusion Detection Intrusion Detection and Vulnerability Assessment Authentication and Non-repudiation Identification and Authentication Insider Threats and Countermeasures Intrusion Detection & Prevention Secure Cloud Computing Security Information Systems Architecture and Design and Security Patterns Security Management Security Requirements (threats, vulnerabilities, risk, formal methods, etc.) Sensor and Mobile Ad Hoc Network Security Service and Systems Design and QoS Network Security Software Security Security and Privacy in Mobile Systems Security and Privacy in Pervasive/Ubiquitous Computing Security and Privacy in Web Sevices Security and Privacy Policies Security Area Control Security Deployment Security Engineering Security for Grid Computing Security in Distributed Systems Paper Submission Authors are invited to submit papers for this journal through Submission system. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For paper format download the template in this page.
Views: 12 ijcis journal
A Machine Learning Approach For Identifying Disease-Treatment Relations In Short Texts - 2011
 
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http://www.mtechprojects.com - A Machine Learning Approach For Identifying Disease-Treatment Relations In Short Texts - 2011 - MTechProjects.com offering final year academic projects, MS, ME, M.Tech, MCA, BE, B.Tech, and PhD Students, Mtech MS ME MEng M.Sc Projects in Hyderabad,bangalore,chennai and delhi,india http://www.mtechprojects.com
Views: 376 MTech Project
International Journal of Network Security & Its Applications (IJNSA)
 
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International Journal of Network Security & Its Applications (IJNSA) ISSN 0974 - 9330 (Online); 0975 - 2307 (Print) http://airccse.org/journal/ijnsa.html Scope & Topics The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas. * Network and Wireless Network Security * Mobile, Ad Hoc and Sensor Network Security * Peer-to-Peer Network Security * Database and System Security * Intrusion Detection and Prevention * Internet Security & Applications * Security & Network Management * E-mail security, Spam, Phishing, E-mail fraud * Virus, worms, Trojon Protection * Security threats & countermeasures (DDoS, MiM, Session * Hijacking,Replay attack etc,) * Ubiquitous Computing Security * Web 2.0 security * Cryptographic protocols * Performance Evaluations of Protocols & Security Application Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijnsa.html
Views: 15 aircc journal
CLOUD DATA SECURITY CLOUDSIM PROJECTS
 
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Views: 1297 PHD Projects
A Distributed Ensemble Approach For Mining Healthcare Data Under Privacy Constraints
 
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A Distributed Ensemble Approach For Mining Healthcare Data Under Privacy Constraints -IEEE PROJECTS 2017-2018 MICANS INFOTECH PVT LTD, CHENNAI ,PONDICHERRY http://www.micansinfotech.com http://www.finalyearprojectsonline.com http://www.softwareprojectscode.com +91 90036 28940 ; +91 94435 11725 ; [email protected] Download [email protected] http://www.micansinfotech.com/VIDEOS.html ABSTRACT: Hybrid clouds have gained popularity in recent times in a variety of organizations due to their ability to provide additional capacity in a public cloud, to augment private cloud capacity, when it is needed. However, scheduling distributed applications' jobs (e.g, workflow tasks) on hybrid cloud resources introduces new challenges. One key problem is the danger of exposing private data and jobs in a third-party public cloud infrastructure, for example in healthcare applications. In this article, we tackle the problem of designing workflow scheduling algorithms to meet customers' deadlines, while not compromising data and task privacy requirements. Our work is different from most studies on workflow scheduling where the main goal is to achieve a balance between desirable, yet incompatible constraints, such as meeting the deadline and/or minimizing the execution time. Although many others have addressed the trade-off between cost and time, or privacy and cost, their work still suffers from an insufficient consideration of the trade-off between privacy and time. To address such shortcomings in the literature, we present a new SaaS scheduling broker composed of MPHC-P1, MPHCP2, and MPHC-P3 policies to preserve privacy while scheduling the workflows' tasks under customers' deadlines. We evaluated our approach using real workflows running on a VMware based hybrid cloud. Results demonstrate that under our scheduling policies, MPHC-P2 and MPHC-P3 are promising in time-critical scenarios by reducing the total cost by 10-20 percent compared to alternatives. Overall, results show that our approach is efficient in reducing the cost of executing workflows while satisfying both their privacy and deadline constraints.
What is HONEYTOKEN? What does HONEYTOKEN mean? HONEYTOKEN meaning, definition & explanation
 
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What is HONEYTOKEN? What does HONEYTOKEN mean? HONEYTOKEN meaning - HONEYTOKEN pronunciation - HONEYTOKEN definition - HONEYTOKEN explanation - How to pronounce HONEYTKEN? Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ In the field of computer security, honeytokens are honeypots that are not computer systems. Their value lies not in their use, but in their abuse. As such, they are a generalization of such ideas as the honeypot and the canary values often used in stack protection schemes. Honeytokens do not necessarily prevent any tampering with the data, but instead give the administrator a further measure of confidence in the data integrity. Honeytokens are fictitious words or records that are added to legitimate databases. They allow administrators to track data in situations they wouldn't normally be able to track, such as cloud based networks. If data is stolen, honey tokens allow administrators to identify who it was stolen from or how it was leaked. If there are three locations for medical records, different honey tokens in the form of fake medical records could be added to each location. Different honeytoken would be in each set of records. If they are chosen to be unique and unlikely to ever appear in legitimate traffic, they can also be detected over the network by an intrusion-detection system (IDS), alerting the system administrator to things that would otherwise go unnoticed. This is one case where they go beyond merely ensuring integrity, and with some reactive security mechanisms, may actually prevent the malicious activity, e.g. by dropping all packets containing the honeytoken at the router. However, such mechanisms have pitfalls because it might cause serious problems if the honeytoken was poorly chosen and appeared in otherwise legitimate network traffic, which was then dropped. As stated by Lance Spitzner in his article on SecurityFocus, the term was first coined by Augusto Paes de Barros in 2003. Honeytokens can exist in many forms, from a dead, fake account to a database entry that would only be selected by malicious queries, making the concept ideally suited to ensuring data integrity. A particular example of a honeytoken is a fake email address used to track if a mailing list has been stolen.
Views: 375 The Audiopedia
Ian Witten Interview - Our Current Explosion of Data
 
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Professor Ian Witten talks an undeniable fact of modern society: the amount of data we are collecting is exploding. This increases the possibilities for data mining, which Weka is designed to help you do. Excerpt from an interview by Class Central with Ian Witten (Professor of Computer Science at the University of Waikato) about his Intro to Data Mining MOOC. See original article here: http://www.blog.class-central.com/?p=57230 Link to Course Information: https://www.class-central.com/mooc/1152/data-mining-with-weka Comprehensive MOOCs listings: https://www.class-central.com/
Views: 461 Class Central
Opinion Mining by Dr. Alsmadi
 
29:00
Symposium of Data Mining Applications (SDMA) 2014. The event is organized by Prince Megrin Data Mining Center (Megdam) presented by Dr. Izzat Alsmadi, associate professor from Prince Sultan University
Views: 317 Megdam Center
Joint Cluster Analysis of Attribute Data and Relationship Data: Problems, Algorithms & Applications
 
01:23:30
Attribute data and relationship data are two principle types of data, representing the intrinsic and extrinsic properties of entities. While attribute data has been the main source of data for cluster analysis, relationship data such as social networks or metabolic networks are becoming increasingly available. In many cases these two data types carry complementary information, which calls for a joint cluster analysis of both data types in order to achieve more natural clusterings. For example, when identifying research communities, relationship data could represent co-author relationships and attribute data could represent the research interests of scientists. Communities could then be identified as clusters of connected scientists with similar research interests. Our introduction of joint cluster analysis is part of a recent, broader trend to consider as much background information as possible in the process of cluster analysis, and in general, in data mining. In this talk, we briefly review related work including constrained clustering, semi-supervised clustering and multi-relational clustering. We then propose the Connected k-Center (CkC) problem, which aims at finding k connected clusters minimizing the radius with respect to the attribute data. We sketch the main ideas of the proof of NP-completeness and present a constant factor approximation algorithm for the CkC problem. Since this algorithm does not scale to large datasets, we have also developed NetScan, a heuristic algorithm that is efficient for large, real databases. We report experimental results from two applications, community identification and document clustering, both based on DBLP data. Our experiments demonstrate that NetScan finds clusters that are more meaningful and accurate than the results of existing algorithms. We conclude the talk with other promising applications and new problems of joint cluster analysis. In particular, we discuss the clustering of gene expression data and the hotspot analysis of crime data as well as a joint cluster analysis problem that does not require the user to specify the number of clusters in advance.
Views: 51 Microsoft Research
Fault Tolerant Clustering Topology Evolution Mechanism of Wireless Sensor Networks
 
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Fault Tolerant Clustering Topology Evolution Mechanism of Wireless Sensor Networks - IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project AI - DEEP LEARNING 1. Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks 2. Derin Ö˘grenmeyle Bir Foto˘grafın Yerini Bulma Finding Location of A Photograph with Deep Learning 3. Learning for Personalised Medicine: A Comprehensive Review from Deep Learning Perspective 4. Person Reidentification via Structural Deep Metric Learning 5. Self-Paced Prioritized Curriculum Learning With Coverage Penalty in Deep Reinforcement Learning AI - NATURAL LANGUAGE PROCESSING ( NLP ) 1. Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites 2. Bag-of-Discriminative-Words (BoDW) Representation via Topic Modeling 3. Weakly-supervised Deep Embedding for Product Review Sentiment Analysis 4. QuantCloud: Enabling Big Data Complex Event Processing for Quantitative Finance through a Data-Driven Execution 5. Automatic Generation of News Comments Based on Gated Attention Neural Networks 6. Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers 7. BreakingNews: Article Annotation by Image and Text Processing 8. Convolutional Neural Network with Pair-Wise Pure Dependence for Sentence Classification 9. Detecting Android Malware Leveraging Text Semantics of Network Flows 10. Identifying Student Difficulty in a Digital Learning Environment 11. Research on Deep Learning Techniques in Breaking Text-based Captchas and Designing Image-based Captcha 12. Rich Short Text Conversation Using Semantic Key Controlled Sequence Generation 13. Synthetic Social Media Data Generation 14. Learning to Extract Action Descriptions fromNarrative Text AI - VISION (IMAGE PROCESSING) 1. Exploratory Visual Sequence Mining Based on Pattern-Growth 2. E-assessment using image processing in ∞Exams 3. A CNN-based Framework for Comparison of Contactless to Contact-based Fingerprints 4. Moving Object Detection from a Moving Stereo Camera via Depth Information and Visual Odometry 5. Recolored Image Detection via a Deep Discriminative Model
SVM-DT-Based Adaptive and Collaborative Intrusion Detection- IEEE PROJECTS 2018
 
15:08
SVM-DT-Based Adaptive and Collaborative Intrusion Detection- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project AI - DEEP LEARNING 1. Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks 2. Derin Ö˘grenmeyle Bir Foto˘grafın Yerini Bulma Finding Location of A Photograph with Deep Learning 3. Learning for Personalised Medicine: A Comprehensive Review from Deep Learning Perspective 4. Person Reidentification via Structural Deep Metric Learning 5. Self-Paced Prioritized Curriculum Learning With Coverage Penalty in Deep Reinforcement Learning AI - NATURAL LANGUAGE PROCESSING ( NLP ) 1. Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites 2. Bag-of-Discriminative-Words (BoDW) Representation via Topic Modeling 3. Weakly-supervised Deep Embedding for Product Review Sentiment Analysis 4. QuantCloud: Enabling Big Data Complex Event Processing for Quantitative Finance through a Data-Driven Execution 5. Automatic Generation of News Comments Based on Gated Attention Neural Networks 6. Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers 7. BreakingNews: Article Annotation by Image and Text Processing 8. Convolutional Neural Network with Pair-Wise Pure Dependence for Sentence Classification 9. Detecting Android Malware Leveraging Text Semantics of Network Flows 10. Identifying Student Difficulty in a Digital Learning Environment 11. Research on Deep Learning Techniques in Breaking Text-based Captchas and Designing Image-based Captcha 12. Rich Short Text Conversation Using Semantic Key Controlled Sequence Generation 13. Synthetic Social Media Data Generation 14. Learning to Extract Action Descriptions fromNarrative Text AI - VISION (IMAGE PROCESSING) 1. Exploratory Visual Sequence Mining Based on Pattern-Growth 2. E-assessment using image processing in ∞Exams 3. A CNN-based Framework for Comparison of Contactless to Contact-based Fingerprints 4. Moving Object Detection from a Moving Stereo Camera via Depth Information and Visual Odometry 5. Recolored Image Detection via a Deep Discriminative Model
International Journal of Digital Crime and Forensics
 
01:25
International Journal of Digital Crime and Forensics Wei Qi Yan (Auckland University of Technology, New Zealand) Now Available Year Established: 2009 Publish Frequency: Quarterly ISSN: 1941-6210 EISSN: 1941-6229 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDCF.20170101 ___________ Description: The International Journal of Digital Crime and Forensics (IJDCF) provides state-of-the-art coverage in the development of legal evidence found in computers and electronic storage mediums. IJDCF addresses the use of electronic devices and software for crime prevention, investigation, and the application of a broad spectrum of sciences to answer questions of interest to the legal system. This fully refereed journal contains high quality theoretical and empirical research articles, research reviews, case studies, book reviews, tutorials, and editorials in this field. ___________ Topics Covered: Computational approaches to digital crime preventions Computer virology Crime scene imaging Criminal investigative criteria and standard of procedure on computer crime Cryptological techniques and tools for crime investigation Data carving and recovery Digital document examination Digital evidence Digital signal processing techniques for crime investigations Identity theft and biometrics Information warfare Machine learning, data mining, and information retrieval for crime prevention and forensics Malicious codes Network access control and intrusion detection Policy, standards, protocols, accreditation, certification, and ethical issues related to digital crime and forensics Practical case studies and reports, legislative developments, and limitations, law enforcement Small digital device forensics (cell phones, smartphone, PDAs, audio/video devices, cameras, flash drives, gaming devices, GPS devices, etc.) Steganography and steganalysis Terrorism knowledge portals and databases Terrorism related analytical methodologies and software tools Terrorist incident chronology databases Watermarking for digital forensics ___________ Abstracted and Indexed in: Web of Science Emerging Sources Citation Index (ESCI) SCOPUS Compendex (Elsevier Engineering Index) INSPEC ACM Digital Library Applied Social Sciences Index & Abstracts (ASSIA) Cabell's Directories DBLP GetCited Google Scholar JournalTOCs Library & Information Science Abstracts (LISA) MediaFinder Norwegian Social Science Data Services (NSD) The Index of Information Systems Journals The Standard Periodical Directory Ulrich's Periodicals Directory Web of Science
Views: 87 IGI Global
NS2 INTRUSION DETECTION WITH ONE SOURCE AND THREE DESTINATION
 
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PG Embedded Systems www.pgembeddedsystems.com #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2015 ieee projects, 2015 ieee java projects, 2015 ieee dotnet projects, 2015 ieee android projects, 2015 ieee matlab projects, 2015 ieee embedded projects, 2015 ieee robotics projects, 2015 IEEE EEE PROJECTS, 2015 IEEE POWER ELECTRONICS PROJECTS, ieee 2015 android projects, ieee 2015 java projects, ieee 2015 dotnet projects, 2015 ieee mtech projects, 2015 ieee btech projects, 2015 ieee be projects, ieee 2015 projects for cse, 2015 ieee cse projects, 2015 ieee it projects, 2015 ieee ece projects, 2015 ieee mca projects, 2015 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2015 mtech projects, 2015 mphil projects, 2015 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2015 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2015 ieee omnet++ projects, ieee 2015 oment++ project, innovative ieee projects, latest ieee projects, 2015 latest ieee projects, ieee cloud computing projects, 2015 ieee cloud computing projects, 2015 ieee networking projects, ieee networking projects, 2015 ieee data mining projects, ieee data mining projects, 2015 ieee network security projects, ieee network security projects, 2015 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2015 wireless networking projects ieee, 2015 ieee web service projects, 2015 ieee soa projects, ieee 2015 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2015 IEEE java projects,2015 ieee Project Titles, 2015 IEEE cse Project Titles, 2015 IEEE NS2 Project Titles, 2015 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2015 - 2015 ... Image Processing. IEEE 2015 - 2015 Projects | IEEE Latest Projects 2015 - 2015 | IEEE ECE Projects2015 - 2015, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2015 IEEE C#, C Sharp Project Titles, 2015 IEEE EmbeddedProject Titles, 2015 IEEE NS2 Project Titles, 2015 IEEE Android Project Titles. 2015 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2015, IEEE 2015 PROJECT TITLES, M.TECH. PROJECTS 2015, IEEE 2015 ME PROJECTS.
Views: 230 PG Embedded Systems
Dahua & Hikvision Ban - Sealed Deal!
 
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The President sign the defense spending bill that includes a ban on Federal installations of Dahua and Hikvision products, including OEM products. Do you think this is legit? Who will replace these vendors? Give me your opinions! White House Briefing: https://www.whitehouse.gov/briefings-statements/president-donald-j-trump-rebuilding-readying-military-defend-threats/ IPVM Article: https://ipvm.com/reports/ban-law 10% off IPVM: https://ipvm.com/members?friend=30302&name=willie-howe H5 Consulting/Contact/Newsletter: http://h5llc.com My Amazon Link: https://www.amazon.com/shop/williehowe Private Internet Access: https://www.privateinternetaccess.com/pages/buy-vpn/howex5 SIP.US: http://h5.sip.us H5 Discord: https://discord.gg/3xyT8NX Netool: https://netool.io use code WILLIEHOWE to save at least 10%! Digital Ocean Referral Link: https://m.do.co/c/39aaf717223f Contact us for network consulting and best practices deployment today! We support all Grandstream, Obihai, Polycom, Plantronics, Ubiquiti Networks, MikroTik, Extreme, Palo Alto, and more! Come back for the next video! Twitter - @WillieHowe Instagram - @howex5 SUBSCRIBE! THUMBS-UP! Comment and Share!
Views: 5965 Willie Howe
Searching ACM Digital Library
 
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Searching ACM Digital Library for research papers on the topic technology affecting friendships
Views: 31 Taylor Friend
#2 Big Data and Online Fraud Prevention with John Omernik~1
 
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-uploaded in HD at http://www.TunesToTube.com
Intro to Big Data, Data Science & Predictive Analytics
 
01:33:19
We introduce you to the wide world of Big Data, throwing back the curtain on the diversity and ubiquity of data science in the modern world. We also give you a bird's eye view of the subfields of predictive analytics and the pieces of a big data pipeline. -- At Data Science Dojo, we're extremely passionate about data science. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f6y7c0 See what our past attendees are saying here: https://hubs.ly/H0f6wPQ0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 12304 Data Science Dojo
Vampire Attacks Draining life from wireless ad hoc sensor networks
 
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2013 IEEE Transaction On Network Security For Technical Details Contact ::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com Bangalore - Karnataka
Views: 3552 manju nath
2011-01-19 CERIAS - Retrofitting Legacy Code for Security
 
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Recorded: 01/19/2011 CERIAS Security Seminar at Purdue University Retrofitting Legacy Code for Security Somesh Jha, University of Wisconsin Research in computer security has historically advocated Design forSecurity, the principle that security must be proactively integratedinto the design of a system. While examples exist in the researchliterature of systems that have been designed for security, there arefew examples of such systems deployed in the real world. Economic andpractical considerations force developers to abandon security andfocus instead on functionality and performance, which are moretangible than security. As a result, large bodies of legacy code oftenhave inadequate security mechanisms. Security mechanisms are added tolegacy code on-demand using ad hoc and manual techniques, and theresulting systems are often insecure.This talk advocates the need for techniques to retrofitsystems with security mechanisms. In particular, it focuses on theproblem of retrofitting legacy code with mechanisms for authorizationpolicy enforcement. It introduces a new formalism, calledfingerprints, to represent security-sensitive operations. Fingerprintsare code templates that represent accesses to security-criticalresources, and denote key steps needed to perform operations on theseresources. This talk develops both fingerprint mining andfingerprint matching algorithms.Fingerprint mining algorithms discover fingerprints ofsecurity-sensitive operations by analyzing source code. Thistalk presents two novel algorithms that use dynamic programanalysis and static program analysis, respectively, to minefingerprints. The fingerprints so mined are used by the fingerprintmatching algorithm to statically locate security-sensitiveoperations. Program transformation is then employed to staticallymodify source code by adding authorization policy lookups at eachlocation that performs a security-sensitive operation.These techniques have been applied to three real-world systems. Thesecase studies demonstrate that techniques based upon program analysisand transformation offer a principled and automated alternative to thead hoc and manual techniques that are currently used to retrofitlegacy software with security mechanisms. Time permitting, we willtalk about other problems in the context of retrofitting legacy codefor security. I will also indicate where ideas from model-checking have been used in this work. Somesh Jha received his B.Tech from Indian Institute of Technology,New Delhi in Electrical Engineering. He received his Ph.D. in ComputerScience from Carnegie Mellon University in 1996. Currently, Somesh Jhais a Professor in the Computer Sciences Department at theUniversity of Wisconsin (Madison), which he joined in 2000. His workfocuses on analysis of security protocols, survivability analysis,intrusion detection, formal methods for security, and analyzingmalicious code. Recently he has also worked on privacy-preservingprotocols. Somesh Jha has published over 100 articles in highly-refereedconferences and prominent journals. He has won numerous best-paper awards.Somesh also received the NSF career award in 2005. (Visit: www.cerias.purude.edu)
Views: 83 ceriaspurdue
S-CAR Dissertation Defense: Adeeb Yousif- Fragmentation of Identity Group in Darfur-Sudan
 
01:46:52
Dissertation Defense - Adeeb Yousif- Fragmentation of Identity Group and Conflict Mitigation in Darfur-Sudan 2005-2015 January 31, 2018 Committee: Dr. Daniel Rothbart (Chair) Dr. Solon Simmons Dr. John Paden Abstract: “Fragmentation of Identity Groups and Conflict Mitigation in Darfur, Sudan 2005-2015” looks at how fragmentation based on situational identity/ethnic affiliation further increases the risk of violence in the region and jeopardizes conflict mitigation in Darfur. This research examines the splitting of rebel groups and its effects on conflict mitigation in Darfur. I argue that identity/ethnic mobilization presents major obstacles to peace and democratic governance; when an armed group mobilizes its members on the basis of identity to fight against an authoritarian regime, they will end up fragmenting. Lack of a clear, united political vision, self-interest, absence and difference in ideologies, and ethnic mobilization were among the important factors that led groups to fragmentation. The studies looked at fragmentation causes, consequences, and its contributing factors. The research also includes fragmentation and conflict mitigation. Rebels fight against other rebels, religious sects, tribes, clans, and families, making conflict mitigation and peace extremely difficult to achieve. The split between the Sudan Liberation Army/Movement (SLA/M) and the Justice and Equality Movement (JEM), now results in 77 factions. This research aims to highlight the dynamics and processes involved in these types of complex, protracted civil wars. For the past eight years, the Government of Sudan(GoS) and the rebel factions have signed 43 peace agreements. Regrettably, none of these agreements were able to bring peace or security to Darfur; rather, they have created more divisions between the signatories, non-signatories and their supporters. The fragmentations also lead to an increase in stakeholders, sparking the interests of other tribes not previously involved. The study utilizes qualitative research, including interviews with rebel leaders and commanders, conflict-affected populations, and civil society organizations, driven by an effort to understand new dynamics and causes of these fragmentation. http://scar.gmu.edu
DEF CON 21- Daniel Burroughs - Open Public Sensors
 
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Open Public Sensors, Trend Monitoring and Data Fusion DANIEL BURROUGHS ASSOCIATE DIRECTOR OF TECHNOLOGY, CENTER FOR LAW ENFORCEMENT TECHNOLOGY, TRAINING AND RESEARCH Our world is instrumented with countless sensors. While many are outside of our direct control, there is an incredible amount of publicly available information being generated and gathered all the time. While much of this data goes by unnoticed or ignored it contains fascinating insight into the behavior and trends that we see throughout society. The trick is being able to identify and isolate the useful patterns in this data and separate it from all the noise. Previously, we looked at using sites such as Craigslist to provide a wealth of wonderfully categorized information and then used that to answer questions such as "What job categories are trending upward?", "What cities show the most (or the least) promise for technology careers?", and "What relationship is there between the number of bikes for sale and the number of prostitution ads?" After achieving initial success looking at a single source of data, the challenge becomes to generate more meaningful results by combining separate data sources that each views the world in a different way. Now we look across multiple, disparate sources of such data and attempt to build models based on the trends and relationships found therein. The initial inspiration for this work was a fantastic talk at DC13, "Meme Mining for Fun and Profit". It also builds upon a similar talk I presented at DC18. And once again seeks to inspire others to explore the exploitation of such publicly available sensor systems. Daniel Burroughs first became interested in computer security shortly after getting a 300 baud modem to connect his C64 to the outside world. After getting kicked off his favorite BBS for "accidently" breaking into it, he decided that he needed to get smarter about such things. Since that time he has moved on to bigger and (somewhat) better things. These have included work in virtual reality systems at the Institute for Simulation and Training at the University of Central Florida, high speed hardware motion control software for laser engraving systems, parallel and distributed simulation research at Dartmouth College, distributed intrusion detection and analysis at the Institute for Security Technology Studies, and the development of a state-wide data sharing system for law enforcement agencies in Florida. Daniel was an associate professor of engineering at the University of Central Florida for 10 years prior to his current position as the Associate Technology Director for the Center for Law Enforcement Technology, Training, & Research. He also is a co-founder of Hoverfly Technologies, an aerial robotics company, and serves on the board of directors for Familab -- a hackerspace located in Orlando. He is also the proud owner of two DefCon leather jackets won at Hacker Jeopardy at DEF CON 8 & 9 (as well as few hangovers from trying to win more).
Views: 979 DEFCONConference
Website / Web Log Analysis - Web Traffic Stats & Page Tracking - Week #26
 
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http://www.26weekplan.com Website / Web Log Analysis - Web Traffic Stats & Page Tracking is Week #26 of the 26-Week Internet Marketing Plan
Views: 1331 David Bain TV
Human Rights Organizations Launch Free Tool to Detect Surveillance Software
 
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More and more, governments are using powerful spying software to target human rights activists and journalists, often the forgotten victims of cyberwar. Now, these victims have a new tool to protect themselves. Called Detekt, it scans a person's computer for traces of surveillance software, or spyware. A coalition of human rights organizations, including Amnesty International and the Electronic Frontier Foundation launched Detekt on Wednesday, with the goal of equipping activists and journalists with a free tool to discover if they've been hacked. http://feeds.mashable.com/~r/Mashable/~3/kDq4tJY2QUE/ http://www.wochit.com
Views: 92 Wochit Tech
Inside the NSA: An Evening with General Michael Hayden
 
01:13:37
After the attacks, at the request of the White House, Hayden intensified and expanded NSA wiretapping operations of various communications between Americans and terrorist suspects abroad in hopes of detecting and preventing another terrorist attack. These initial efforts were executed without a court order and after being revealed by The New York Times, were subsequently placed under judicial review. Over time, the NSA’s efforts grew into the multidimensional programs exposed by Edward Snowden, including the collection and storage of phone and email metadata covering billions of calls and messages between American citizens. In conversation with Amy Zegart, General Hayden provides an insider’s account about the origins and development of the NSA programs. He discusses the directives and mechanisms to control them, and the disagreements within the Bush administration about the extent of the wiretapping. He offers his views on the justification, legal status, scale, and effectiveness of the NSA monitoring.
Views: 21525 Stanford
Algebra.com stream of real time search query logs
 
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This video shows a real-time log of page URLs and search queries (when search engines provide it) that led users to Algebra.Com.
Views: 537 Igor Chudov
[CB16] Who put the backdoor in my modem? by Ewerson Guimaraes
 
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For quite some time we have been seeing espionage cases reaching countries, governments and large companies. A large number of backdoors were found on network devices, mobile phones and other related devices, having as main cases the ones that were reported by the media, such as: TP-Link, Dlink, Linksys, Samsung and other companies which are internationally renowned. This talk will discuss a backdoor found on the modem / router rtn, equipment that has a big question mark on top of it, because there isn’t a vendor identification and no information about who’s its manufacturer and there are at least 7 companies linked to its production, sales and distribution in the market. Moreover, some of them never really existed. Which lead us to question on the research title: “Who put the backdoor in my modem?” -- Ewerson Guimaraes Degree in Computer Science from Fumec University, Security Analyst and Researcher at Epam Systems. Certified by Offesinve Security(OSCP) and Elearn(WPT) as Pentester, Ewerson has published articles in the Brazilian Information Security/Computers magazines H4ck3r and GEEK, moreover, posted exploits and advisory on SecurityFocus found in big companies like: IBM, McAfee, Skype, Technicolor, Tufin, TrendMicro and others. Contrib to develop some modules to Metasploit Framework Project. Founder of BHack Conference and Area31, the first hackerpsace in Minas Gerais and is an active Kali Linux Community Contributor http://codeblue.jp/2016/en/contents/speakers.html#speaker-guimaraes
DOTNET 2012 IEEE Project TOPOLOGY CONTROL IN MOBILE AD HOC NETWORKS WITH COOPERATIVE COMMUNICATIONS
 
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To Get any Project for CSE,IT ECE,EEE Contact Me @9966032699,8519950799 or mail us - [email protected]­m-Visit Our WebSite www.liotechprojects.com,www.iotech.in Cooperative communication has received tremendous interest for wireless networks. Most existing works on cooperative communications are focused on link-level physical layer issues. Consequently, the impacts of cooperative communications on network-level upper layer issues, such as topology control, routing and network capacity, are largely ignored. In this article, we propose a Capacity-Optimized Cooperative (COCO) topology control scheme to improve the network capacity in MANETs by jointly considering both upper layer network capacity and physical layer cooperative communications. Through simulations, we show that physical layer cooperative communications have significant impacts on the network capacity, and the proposed topology control scheme can substantially improve the network capacity in MANETs with cooperative communications.
Views: 464 LT LIOTechprojects
Frost and Sullivan - Retail Security - Discussing the Growth Potential
 
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1. Retail SecurityDiscussing the Growth Potential Satish Lele, Vice President Industrial Practice October 14, 2011 2. About Frost & Sullivan 2 3. The Frost & Sullivan Story Emerging Research Growth Partnership Visionary Innovation 1961– 1961–1990 1990– 1990–Today Today– Today–Future 1961 1990 TodayPioneered Emerging Market Partnership Relationship& Technology Research with Clients Visionary Innovation• Global Footprint Begins • Growth Partnership Services • Mega Trends Research• Country Economic Research • GIL Global Events • CEO 360 Visionary Perspective• Market & Technical Research • GIL University • GIL Think Tanks• Best Practice Career Training • Growth Team Membership • GIL Global Community• MindXChange Events • Growth Consulting • Communities of Practice 3 4. Our Global Footprint 40+ OfficesScanning the Globe for Opportunities and Innovation 4 5. Ice & Sullivan is a vital counseling organization that helps customers growtheir organizations. We have an aggregate of 2000 staff in 40 nations Our Lead Offer: Growth Partnership Services 1 Research Services TEAM 2 Growth Consulting 3 Training Market Engineering Research Growth Consulting Corporate Training & Analyst Briefings Growth Workshop Development Market Insights Whitepapers Industry Tracker Top of Mind Surveys Mover & Shaker Interviews Growth Opportunity Newsletters Client Councils Best Practices Analyst Inquiry Hours Awards Customer Research Economic Impact Articles Country Industry Forecasts Financial Benchmarking & Analysis Technical Insights Research Technical Insights Alerts CLIENT PORTAL & E- BROADCASTS 5 6. Our Industry Coverage Automotive & Transportation Aerospace & Defense Measurement & Consumer Information & Instrumentation Technologies Communication Technologies Automotive Energy & Power Environment & Building HealthcareTransportation & Logistics Systems Technologies Minerals & Mining Chemicals, Materials Electronics & Industrial Automation & Food Security & Process Control 6 7. Ice & Sullivan Physical Security Practice Coverage PHYSICAL SECURITY/ ACCESS CONTROL MARKET Electronic Access Control Systems Surveillance and Others (EACS) – Hardware Monitoring • Intrusion Detection System • Card access • Video Surveillance Systems (IDS) • Proximity cards (RFID) & • RFID, RTLS, EAS • Cameras (Analog & IP) perusers • Alarm Monitoring • DVR • Contactless Smart cards • Biometrics • NVR & perusers • Perimeter Protection • Video Server • Keypad • Software • Video Analytics • Biometrics • Encoders • Smart Cards • Integrated Readers • Software • Other EACS equipment: Controllers, EM lock, and so on Source : Frost & Sullivan 7 8. Center Points Retail SecurityOverview of retail industry- Challenges and DriversTechnology AssessmentRegional Analysis of the Retail industryPricing TrendsAsia Pacific Growth PotentialFuture Outlook for Retail security 8 9. Outline 9 10. Worldwide Retail Industry Global Retail Sales Forecasts 2014 India and China driving the development in 2013 retail salesYear 2012 2011 2010 0.0 2.0 4.0 US$ Trillion 6.0 8.0 10.0 W. Europe Americas Asia Pacific Source: PWC Publication 2010• China is the world's second biggest retail advertise after the United States.• China encountered a year-on- year retail deals development of more than 14% in 2010.• Emerging markets fuelled by expanding customer spending and expanded universal retailers foot shaped impression. I• Medium development of 5-7% due a harmony between soaked western European and becoming eastern European retail division. 10 11. Real Drivers 1-2 Years 3-4 Years 5-7 YearsIncreased concentrate on misfortune counteractive action to lessen retailshrinkage . High HighThe requirement for adaptable creative arrangements and fantastic High Highcustomer administration drive interest for securityIP innovation to achieve coordinated frameworks andremote observing Medium High HighIncreasing foot shaped impression of worldwide retailers expandsopportunities for security merchants in developing markets Medium High HighUse of security arrangements as business and administration Low Medium Hightool opens up new roads for vendorsDecreasing expense of most recent innovation in security space Low Medium Highdrives speculation from the cost cognizant retailers 11 12. Significant Challenges Challenge 1-2 Years 3-4 Years 5-7 YearsRetailer's necessity for tailor-made arrangements High Mediumincreases the requirement for customizationLow mindfulness among retailers about latesttechnology hampers the appropriation rate for the same High MediumHigh impact of dispersion channel accomplices on end-client choices requires solid merchant accomplice High MediumrelationIncrease in the quantity of chiefs included inprocuring security. Medium LowThe accessibility of minimal effort security gear has Medium Lowmade value the key differentiator 12
Views: 38 DailySlides
DEF CON 22 - XlogicX & chap0  - Abuse of Blind Automation in Security Tools
 
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Eric (XlogicX) Davisson and Ruben Alejandro (chap0) - Abuse of Blind Automation in Security Tools Slides Here: https://defcon.org/images/defcon-22/dc-22-presentations/Davisson-Alejandro/DEFCON-22-Eric-Davisson-Ruben-Alejandro-Abuse-of-Blind-Automation-in-Security-Tools.pdf Abuse of Blind Automation in Security Tools Eric (XlogicX) Davisson SECURITY RESEARCHER Ruben Alejandro (chap0) SECURITY RESEARCHER It is impossibly overwhelming for security personnel to manually analyze all of the data that comes to them in a meaningful way. Intelligent scripting and automation is key. This talk aims to be a humorous reminder of why the word “intelligent” really matters; your security devices might start doing some stupid things when we feed them. This talk is about abusing signature detection systems and confusing or saturating the tool or analyst. Some technologies you can expect to see trolled are anti-virus, intrusion detection, forensic file carving, PirateEye (yep), grocery store loyalty cards (huh?), and anything we can think of abusing. Expect to see some new open-source scripts that you can all use. The presenters don't often live in the high-level, so you may see the terminal, some hex and bitwise maths, raw signatures, and demonstrations of these wacky concepts in action. We don't intend to present dry slides on “hacker magic” just to look 1337. We want to show you cool stuff that we are passionate about, stuff we encourage everyone to try themselves, and maybe inspire new ideas (even if they're just pranks...especially). Eric has obtained degrees in computer engineering, business, and criminal justice. He has SANS certifications for GCIH, GCIA and is currently studying for GREM. This isn't so important to Eric, however, this is the type of thing we like seeing in bios. His interest is in the obscure. While having a basic grip on the general XSS, SQLi, Buffer Overflow (OWASP top whatever), he finds obscurity much more interesting; it's true adventure to him. He enjoys all things low level (and would argue all hackers should), this means he has an “amateur” background in embedded/assembly and does some ignorant EE stuff. He also tries to replace every script with a well crafted regular expression. Eric currently resides in Phoenix Arizona. He is active in his local 2600 community. Finally, he has fond memories of DEFCON at Alexis Park. Twitter: @XlogicX Ruben Alejandro has professional experience in security along with some of the certifications that come with it. His interests a geared to the offensive side of security; he's made some contributions to metasploit and exploitdb. He is really into the community and doesn't want to bore anyone with anymore InfoSec in this bio, he just looks forward to chatting with everyone at the con and having a good time. Twitter: @_chap0
Views: 1670 DEFCONConference
USENIX Enigma 2016 - Data Integrity Based Attacks in Investigative Domains
 
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Eric W. D. Rozier, Assistant Professor of EECS, University of Cincinnati The Trustworthy Data Engineering Laboratory (TRUST Lab) has been working with the World Bank, the Federal Bureau of Investigation (FBI), the Environmental Protection Agency (EPA), and the City of Cincinnati to help solve a common problem faced by many organizations involved in data driven investigations: companies and entities that attempt to disguise malicious activities through attacks on the integrity of available data. In this talk we will explore the challenge of assuring data integrity in heterogenous data systems that face the challenges of velocity, variety, and volume that accompany the domain of Big Data. We will examine real case studies in debarrment and corruption in international procurement with the World Bank, investigations into violations of the Foreign Corrupt Practices Act with the FBI, cases of violations of the Resource Conservation and Recovery Act with the EPA, and human rights abuses of low income citizens by corporate slum-lords in the city of Cincinnati. In each of these cases we will show how malicious actors manipulated the data collection and data analytics process either through misinformation, abuse of regional corporate legal structures, collusion with state actors, or knowledge of underlying predictive analytics algorithms to damage the integrity of data used by machine learning and predictive analytic processes, or the outcomes derived from these processes, to avoid regulatory oversite, sanctions, and investigations launched by national and multi-national authorities. Sign up to find out more about Enigma conferences: https://www.usenix.org/conference/enigma2016#signup Watch all Enigma 2016 videos at: http://enigma.usenix.org/youtube
TWOdW: IBM Java 7 knowledge path, RST in Ruby, continuous integration in agile
 
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New developerWorks content highlights for the week of Aug 15-22. John Swanson joins Scott Laningham for a summary of new content on continuous integration in Agile development, understanding representational state transfer in Ruby, and a new knowledge path on IBM Java 7. Calvin Powers is the feature dW staff guest and talks about editing the new security topic area on developerWorks, IBM's premier resource for developers worldwide with tools, code, and education on IBM products and open standards technology. More at http://www.ibm.com/developerworks
Views: 163 IBM Developer
Packet analyzer
 
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A packet analyzer (also known as a network analyzer, protocol analyzer or packet sniffer, or for particular types of networks, an Ethernet sniffer or wireless sniffer) is a computer program or a piece of computer hardware that can intercept and log traffic passing over a digital network or part of a network. As data streams flow across the network, the sniffer captures each packet and, if needed, decodes the packet's raw data, showing the values of various fields in the packet, and analyzes its content according to the appropriate RFC or other specifications. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 289 Audiopedia
IEEE 2014 JAVA MULTIPLE SOURCE LOCALIZATION IN WIRELESS SENSOR NETWORKS BASED ON TIME
 
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PG Embedded Systems www.pgembeddedsystems.com #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2013 ieee projects, 2013 ieee java projects, 2013 ieee dotnet projects, 2013 ieee android projects, 2013 ieee matlab projects, 2013 ieee embedded projects, 2013 ieee robotics projects, 2013 IEEE EEE PROJECTS, 2013 IEEE POWER ELECTRONICS PROJECTS, ieee 2013 android projects, ieee 2013 java projects, ieee 2013 dotnet projects, 2013 ieee mtech projects, 2013 ieee btech projects, 2013 ieee be projects, ieee 2013 projects for cse, 2013 ieee cse projects, 2013 ieee it projects, 2013 ieee ece projects, 2013 ieee mca projects, 2013 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2013 mtech projects, 2013 mphil projects, 2013 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2013 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2013 ieee omnet++ projects, ieee 2013 oment++ project, innovative ieee projects, latest ieee projects, 2013 latest ieee projects, ieee cloud computing projects, 2013 ieee cloud computing projects, 2013 ieee networking projects, ieee networking projects, 2013 ieee data mining projects, ieee data mining projects, 2013 ieee network security projects, ieee network security projects, 2013 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2013 wireless networking projects ieee, 2013 ieee web service projects, 2013 ieee soa projects, ieee 2013 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2013 IEEE java projects,2013 ieee Project Titles, 2013 IEEE cse Project Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2013 - 2013 ... Image Processing. IEEE 2013 - 2013 Projects | IEEE Latest Projects 2013 - 2013 | IEEE ECE Projects2013 - 2013, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2013 IEEE C#, C Sharp Project Titles, 2013 IEEE EmbeddedProject Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE Android Project Titles. 2013 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2013, IEEE 2013 PROJECT TITLES, M.TECH. PROJECTS 2013, IEEE 2013 ME PROJECTS.
Penelope Boston - Subsurface Astrobiology: Cave Habitat on Earth, Mars, and Beyond
 
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NASA Ames 2016 Summer Series. In our quest to explore other planets, we only have our own planet as an analogue to the environments we may find life. By exploring extreme environments on Earth, we can model conditions that may be present on other celestial bodies and select locations to explore for signatures of life. Dr. Penelope Boston, the Director of the NASA Astrobiology Institute at Ames, describes her work in some of Earth’s most diverse caves and how they inform future exploration of Mars and the search for life in our solar system. For more information about the Office of the Chief Scientist at NASA Ames, please visit http://www.nasa.gov/ames/ocs
2016 EIS Conference Langmuir Lecture
 
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Dr. Margaret Hamburg, an internationally recognized leader in public health and medicine presented the 2016 Langmuir Lecture. As Commissioner of the U.S. Food and Drug Administration, she’s known for advancing regulatory science, modernizing regulatory pathways, and globalization of the agency. She’s also served in leadership capacities at the Nuclear Threat Initiative; Health and Human Services, National Institute of Allergy and Infectious Disease, and as Health Commissioner for New York City. Comments on this video are allowed in accordance with our comment policy: http://www.cdc.gov/SocialMedia/Tools/CommentPolicy.html This video can also be viewed at https://www.cdc.gov/eis/videos/langmuir-lecture-hamburg-2016-lowres.mp4
New Economic and Political Model to Change the Global Profit Culture of Excessive Greed & Corruption
 
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Press CC button for the SUBTITLES (bottom right of the video). For convenience CLICK ON TIME STAMPS: LEAD-IN TO PILLAR ONE: The Resource Oriented Economy to Peg our Currencies with; and the Supply-Demand-Resupply Inventory Network as the Job Creator = (34:51) PILLAR TWO: The People’s Power over Money and Credit—using Public Banks along with the Universal Single Payer system to Compensate Us All = (1:23:20) PILLAR THREE: The Culture of Transparency and Sharing—Open Patents, Sources, Information…Open Everything…along with the Free Public Neutral Internet = (2:37:23) DESCRIPTION: Details to Change Our Global Economic and Political Corporatocracy Culture; so that we reasonably transition the power and the levers of control from the Establishment to the hands of the People! READ THIS ARTICLE: Ascending The Globe Series Part 1: A Revelation for Mankind By Edward D.R. James / http://ascendingtheglobe.com PLEASE TRANSLATE THIS VIDEO TO OTHER LANGUAGES; and Let's Ascend the Global Economic and Political Culture...Together!!! FOLLOW ME: Twitter: https://twitter.com/edwarddrjames Facebook: https://www.facebook.com/EdwardD.R.James Instagram: https://www.instagram.com/edwarddrjames Google+: https://plus.google.com/102613747434654135198
Views: 436 Edward D. R. James
CIGIE Conference, July 11, 2018:  Building on 40 Years of Excellence in Independent Oversight
 
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Inspectors General are Building on 40 Years of Excellence in Independent Oversight. Learn more: https://www.ignet.gov/2018-commemoration. 12:00 Welcome – Carol Ochoa and Dave Berry, Conference Co-Chairs 30:00 Opening Remarks – Senator Charles Grassley 59:30 Keynote Address, “The Real Legacy of Watergate” – Bob Woodward, Associate Editor, Washington Post 1:44:14 Panel Discussion – Commemorating 40 years of Impact Gaston Gianni, Former Inspector General, Federal Deposit Insurance Corporation Mary Kendall, Acting Inspector General, Department of the Interior David Williams, Former Inspector General, United States Postal Service Scott MacFarlane, NBC-4 Washington (Moderator) 2:26:30 Panel Discussion – Congressional Views on Independent Oversight Senator Ron Johnson, Chairman, Senate Homeland Security and Governmental Affairs Committee Senator Heidi Heitkamp, Senate Homeland Security and Governmental Affairs Committee Paul Light, Goddard Professor of Public Service, NYU (Moderator) 4:37:38 Panel Discussion – Agency and Outside Perspectives on Independent Oversight William Barr, Former U.S. Attorney General Sylvia Burwell, Former HHS Secretary and OMB Director Martha Minow, Harvard Law School Glenn Fine, Acting Inspector General, Department of Defense (Moderator) 5:36:37 Panel Discussion –The Pillars of Independent Oversight David Apol, Acting Director, Office of Government Ethics Henry Kerner, Special Counsel, Office of Special Counsel Daniel Levinson, Inspector General, Department of Health and Human Services Max Stier, Partnership for Public Service (Moderator) 6:53:00 Panel Discussion – Preparing for the Next 40 years of Independent Oversight Margaret Weichert, Deputy Director for Management, Office of Management and Budget Michael Horowitz, Inspector General, Department of Justice/CIGIE Chair Peg Gustafson, Inspector General, Department of Commerce Susan Gibson, Inspector General, National Reconnaissance Office Carrie Johnson, National Public Radio (Moderator) 7:55:00 Closing – Michael Horowitz, Inspector General, Department of Justice/CIGIE Chair This video features footage from C-SPAN, Storyblocks, the Jimmy Carter Library, the Reagan Library, the GPO, CIGIE and various Offices of Inspectors General. The footage is used with permission or is considered to be in the public domain. Stock media from Audioblocks | www.audioblocks.com | Composer Patrick Smith and Publisher Electric Philharmonic | Composer Mikael Manvelyan | Composer C. Zatta and Publisher SkillMedia Master | Composer Jason Donnelly and Publisher Music Design by Jason Inc. | Composer Joel Thomas Hunger and Producer Zec Music Ltd.
Views: 483 IGNet Admin
Helen Nissenbaum ─ Must Privacy Give Way to Use Regulation?
 
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Skip ahead to main speaker at 4:30 Helen Nissenbaum, Media, Culture, and Communication, New York University Projected benefits of data science and the paradigm of big data are not compatible with the regulation of information collection prompted by concerns over privacy. So goes a compelling and popular argument. Dr. Nissenbaum will challenge this perspective not only because it plays suspiciously well with the dominant business model of the commercial information industry but also because it rests on misconceptions and ambiguities of key terms. She will unravel the debate between those who continue to see value in protecting privacy and those who would forgo privacy in favor of use regulation. There is no denying some of the genuine and unprecedented challenges to privacy posed by data science, but letting it go will undermine a cornerstone of individual freedom. Sponsored by the Brown University Executive Master in Cybersecurity. Brown University's Executive Master in Cybersecurity is a 16-month Master's degree program for mid-career professionals delivered as a blend of online and face-to-face learning. The program is designed to cultivate high-demand, cross-industry executives with the unique and critical ability to devise and execute integrated, comprehensive cybersecurity strategies for nations and industries across the globe. To learn more about the program, visit brown.edu/cybersecurity.