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Intrusion Detection based on KDD Cup Dataset
 
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Final Presentation for Big Data Analysis
Views: 7195 Qiankun Zhuang
Last Minute Tutorials | KDD | Knowledge Discovery of Data
 
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NOTES:- Theory of computation : https://viden.io/knowledge/theory-of-computation?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 DAA(all topics are included in this link) : https://viden.io/knowledge/design-and-analysis-of-algorithms-topic-wise-ada?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Advanced DBMS : https://viden.io/knowledge/advanced-dbms?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 for QM method-https://viden.io/knowledge/quine-mccluskey-method-qm-method?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 K-MAPS : https://viden.io/knowledge/k-maps-karnaugh-map?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Basics of logic gates : https://viden.io/knowledge/basics-of-logic-gates-and-more?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Website: https://lmtutorials.com/ Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ For any queries or suggestions, kindly mail at: [email protected]
Views: 12978 Last Minute Tutorials
KDD-CUP 99
 
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Solution foR KDD-CUP 99
Views: 2024 QIQI SHI
Data Mining   KDD Process
 
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KDD - knowledge discovery in Database. short introduction on Data cleaning,Data integration, Data selection,Data mining,pattern evaluation and knowledge representation.
KDD-CUP 99 problem
 
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KDD-CUP 99 PROBLEM solutions
Views: 1495 QIQI SHI
KDD99 Data Set Analysis
 
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Analyze KDD99 data set by Sean Han
Views: 3303 Xiao Han
Data Mining for Network Intrusion Detection
 
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Data Mining for Network Intrusion Detection: Experience with KDDCup’99 Data set
Views: 1077 Jessy Li
KDD Cup Classification
 
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Big Data Final
Views: 255 Freddie Zhang
12.2: Programming KDD99 with Keras TensorFlow, Intrusion Detection System (IDS) (Module 12, Part 2)
 
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Creating an intrusion detection system (IDS) with Keras and Tensorflow, with the KDD-99 dataset. This video is part of a course that is taught in a hybrid format at Washington University in St. Louis; however, all the information is online and you can easily follow along. T81-558: Application of Deep Learning, at Washington University in St. Louis Please subscribe and comment! Follow me: YouTube: https://www.youtube.com/user/HeatonResearch Twitter: https://twitter.com/jeffheaton GitHub: https://github.com/jeffheaton More links: Complete course: https://sites.wustl.edu/jeffheaton/t81-558/ Complete playlist: https://www.youtube.com/playlist?list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN
Views: 2922 Jeff Heaton
KDD Cup 1999 Dataset and hmmlearn
 
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https://github.com/muhakbaryasin/HMM-learn-kdd-network-traffic-dataset
Views: 390 akbar E-z
How to Improve the Performance of SVM Classifier | Data Mining
 
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Data Mining Algorithm and intrusion detection system IDS algorithm is being tested in NSL-KDD data-set. I have applied an adaptive learning technique to optimize the output this time... For making our system more efficient and able to generate more accurate result, it is necessary to improve the performance of SVM classifier. Because all the result’s accuracy depends upon data which is generated by SVM Classifier. So when the performance of SVM classifier will improve then our results will be closer to the facts automatically. For any further help contact us at [email protected] visit us at http://www.researchinfinitesolutions.com/ Direct at :: +91-6239359461 Whatsapp at :: +91-6239359461
Views: 5839 Fly High with AI
KDD presentation for Sun Xing
 
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KDD presentation for Sun Xing, analyze KDD 99 data set.
Views: 105 Xiao Han
Analysis of Intrusion Detection from KDD Cup 99 Dataset both Labelled and Unlabelled
 
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Title: Analysis of Intrusion Detection from KDD Cup 99 Dataset both Labelled and Unlabelled Domain: Data Mining Description: Intrusion Detection is one of the high priorities & the challenging tasks for network administrators & security experts. Intrusion detection system is employed to protect the data integrity, confidentiality and system availability from attacks. Data mining has been used extensively and broadly by several network organizations. IDS use the data mining techniques to analyze the resources from the database over a network. It is also necessary to develop a robust algorithm to generate effective rules for detecting the attacks. In this paper a flexible architectural system is proposed that uses Associative Classification (AC) method called Multi-label Classifier based Associative Classification (MCAC) to get better results in terms of accuracy, false alarm rate, efficiency, capability to detect new type of attacks. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in cloud computing 4. 2017 – 2018 cloud computing projects 5. 2017 – 2018 cloud simulation projects 6. 2017 – 2018 cloud sim projects 7. 2017 – 2018 best project center in Chennai 8. Ieee cloud simulation projects in Chennai 9. 2017 – 2018 real time cloud hosting 10. 2017 – 2018 aws console ieee projects 11. aws console real time ieee projects 12. ieee projects deployment in aws console 13. cloud computing projects in amazon cloud server 14. ieee projects in Amazon cloud server 15. amazon cloud server ieee projects 16. 2017 ieee real time cloud projects 17. Cloud sim projects in cloud computing 18. ieee projects in green computing 19. ieee projects in cloud computing 20. green computing ieee projects 21. ieee projects in big data 22. ieee projects in hadoop 23. ieee projects in mango db 24. mango db ieee projects 25. hadoop projects in cloud computing 26. cloud sim projects for final year b.e students 27. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 28. 2017 – 2018 b.e projects 29. 2017 – 2018 m.e projects 30. 2017 – 2018 final year projects 31. Affordable final year projects 32. Latest final year projects 33. Best project center in Chennai, Coimbatore, Bangalore, and Mysore 34. 2017 Best ieee project titles 35. Best projects in java domain 36. Free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 37. 2017 – 2018 ieee base paper free download 38. 2017 – 2018 ieee titles free download 39. best ieee projects in affordable cost 40. ieee projects free download 41. 2017 cloud sim projects 42. 2017 ieee projects on cloud sim 43. 2017 final year cloud sim projects 44. 2017 cloud sim projects for b.e 45. 2017 cloud sim projects for m.e 46. 2017 latest cloud sim projects 47. latest cloud sim projects 48. latest cloud sim projects in java 49. cloud computing cloud sim projects 50. green computing projects in cloud sim 51. mini projects on big data 52. 2017 mini projects on big data 53. final year project on big data 54. big data topics for project 55. ieee projects based on big data 56. big data project ieee ideas for students 57. big data projects for engineering students 58. 2017 ieee big data projects 59. ieee projects on big data 2017 60. cloud computing projects java source code 61. cloud computing projects with source code 62. 2017 mini project on cloud computing 63. cloud computing mini projects 64. mini projects in java 65. ieee projects on cloud computing 66. projects based on cloud computing 67. best cloud computing projects 68. cloud computing project topics 69. major projects on cloud computing 70. final year projects for cse 71. ieee cloud computing projects for cse 72. cloud computing mini project 73. cloud computing related projects 74. cloud computing based projects for final year 75. cloud computing based project ideas 76. cloud computing application projects 77. cloud computing project topics in java 78. cloud computing projects for engineering students
Views: 2224 InnovationAdsOfIndia
Final Year Projects | Effective Analysis of KDD data for Intrusion Detection
 
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Final Year Projects | Effective Analysis of KDD data for Intrusion Detection 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: 4625 Clickmyproject
Building an intrusion detection system using a filter-based feature selection algorithm
 
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Building an intrusion detection system using a filter-based feature selection algorithm in Java TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.
Views: 3790 jpinfotechprojects
Testing and Training of Data Set Using Weka
 
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how to train and test data in weka data mining using csv file
Views: 9693 Tutorial Spot
How to run Snort IDS on DARPA dataset.
 
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In this tutorial I'll show how to run the Snort IDS on DARPA dataset
Views: 3537 basant subba
CloudVista Demo: visualizing the extended KDD Cup 1999 Dataset
 
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CloudVista: visualizing the clustering structure in the "extended" KDD Cup 1999 training data, which has about 40 million records, 41 dimensions, 13.5GB in total. The original dataset has about 4 million records. The additional records in the extended version are generated by randomly picking one original record and adding a random noise to each dimension. The extended version is used to show how larger datasets can be possibly visualized in the CloudVista system. Frame rate: 4 frames per second The original dataset can be found at http://archive.ics.uci.edu/ml/datasets/KDD+Cup+1999+Data
Views: 871 gtkeke
Enter a KDD Cup or Kaggle Competition. You Don't Need to Be an Expert!
 
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In this on-demand webinar, we will show you how TreeNet Gradient Boosting can be used for the 2009 KDD Cup competition to quickly achieve a place in the top 5. At the end of this webinar, our goal is that you will be able to build a TreeNet model that can bring you within decimal places of a winning solution. Use this information as a starting point for Kaggle competitions and other KDD Cup competitions. The software, datasets, and a step-by-step tutorial on how to recreate the results shown can be accessed here: http://info.salford-systems.com/kaggle-competition-kddcup-0 http://www.salford-systems.com
Views: 894 Salford Systems
KDDCup 1999--Yan Zhao
 
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Big Data Final Project
Views: 198 Yan Zhao
KDD Cupset Intrusion Detection DataSet Import to MYSQL Database - Simpleway How to use KDD Cupset
 
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This tutorial tells you how to import KDD Cupset in MYSQL Database INTRUSION DETECTOR LEARNING http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html www.mysqldumper.net/‎ This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 The Fifth International Conference on Knowledge Discovery and Data Mining. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between ``bad'' connections, called intrusions or attacks, and ``good'' normal connections. This database contains a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment.
Application of Machine Learning Techniques for Network Intrusion Detection System - DEMO
 
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Due to the increasing number of attacks in cyberspace. Network intrusion detection becomes a more difficult task. Many Network Intrusion Detection System uses data mining and machine learning techniques. Most researchers use dataset KDD Cup 99, which have been widely criticized for not being able to display the current network situation. In this project, we use a new dataset of networked called the Kyoto 2006+. In this dataset. All data is labeled as normal connections, attack connections and unknown attack. This project compares the prediction results from three algorithms, Decision Tree, Neural Network and K-Nearest Neighbor. In summary will uses the Decision Tree (J48) algorithm to group network connections. This can be used with network intrusion detection systems. For Model training and Model testing. This project uses 269,330 network connection samples. The generated rule works with 98.0878% accuracy. Then, the generated rule implements a program that predicts input data from file and displays the confusion matrix, which uses C language for coding.
Neo Metrics, premiada en la KDD CUP 2007
 
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.- La empresa española Neo Metrics ha resultado premiada en la décima edición de la KDD CUP 2007, la más importante y prestigiosa competición anual de data mining y análisis de bases de datos.
Views: 1311 IDGtv
Application of Machine Learning Techniques for Network Intrusion Detection System - FULL Version
 
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Due to the increasing number of attacks in cyberspace. Network intrusion detection becomes a more difficult task. Many Network Intrusion Detection System uses data mining and machine learning techniques. Most researchers use dataset KDD Cup 99, which have been widely criticized for not being able to display the current network situation. In this project, we use a new dataset of networked called the Kyoto 2006+. In this dataset. All data is labeled as normal connections, attack connections and unknown attack. This project compares the prediction results from three algorithms, Decision Tree, Neural Network and K-Nearest Neighbor. In summary will uses the Decision Tree (J48) algorithm to group network connections. This can be used with network intrusion detection systems. For Model training and Model testing. This project uses 269,330 network connection samples. The generated rule works with 98.0878% accuracy. Then, the generated rule implements a program that predicts input data from file and displays the confusion matrix, which uses C language for coding.
An Internal Intrusion Detection and Protection System by Using Data Mining and Forensic Techniques
 
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Including Packages ======================= * Base Paper * 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: 771 Clickmyproject
How to do the Knowledge Discovery (KDD) process in WEKA using Knowledge Flow
 
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In this video i'll be guiding how to perform WEKA knowledge flow using the KDD process.
Views: 2639 Ranji Raj
KDD99 - Machine Learning for Intrusion Detectors from attacking data
 
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Machine Learning for Intrusion Detectors from attacking data
Views: 3898 Chao-Hung Chen
A Simple Method to Predict Affiliation Ranking in KDDCup 2016
 
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Author: Ping-I Chou, Trend Micro Inc. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 43 KDD2016 video
DataAnalytics KDD Intro 20140922
 
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Big Data - Data mining is also sometimes called Knowledge Discovery in Databases (KDD).
Views: 215 Training
Datamining - Dataset Iris
 
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Datamining
Views: 69 Giuseppe Accardo
2000-10-11 CERIAS - Developing Data Mining Techniques for Intrusion Detection: A Progress Report
 
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Recorded: 10/11/2000 CERIAS Security Seminar at Purdue University Developing Data Mining Techniques for Intrusion Detection: A Progress Report Wenke Lee, North Carolina State University Intrusion detection (ID) is an important component of infrastructure protection mechanisms. Intrusion detection systems (IDSs) need to be accurate, adaptive, extensible, and cost-effective. These requirements are very challenging because of the complexities of today's network environments and the lack of IDS development tools. Our research aims to systematically improve the development process of IDSs. In the first half of the talk, I will describe our data mining framework for constructing ID models. This framework mines activity patterns from system audit data and extracts predictive features from the patterns. It then applies machine learning algorithms to the audit records, which are processed according to the feature definitions, to generate intrusion detection rules. This framework is a "toolkit" (rather than a "replacement") for the IDS developers. I will discuss the design and implementation issues in utilizing expert domain knowledge in our framework. In the second half of the talk, I will give an overview of our current research efforts, which include: cost-sensitive analysis and modeling techniques for intrusion detection; information-theoretic approaches for anomaly detection; and correlation analysis techniques for understanding attack scenarios and early detection of intrusions. Wenke Lee is an Assistant Professor in the Computer Science Department at North Carolina State University. He received his Ph.D. in Computer Science from Columbia University and B.S. in Computer Science from Zhongshan University, China. His research interests include network security, data mining, and workflow management. He is a Principle Investigator (PI) for research projects in intrusion detection and network management, with funding from DARPA, North Carolina Network Initiatives, Aprisma Management Technologies, and HRL Laboratories. He received a Best Paper Award (applied research category) at the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), and Honorable Mention (runner-up) for Best Paper Award (applied research category) at both KDD-98 and KDD-97. He is a member of ACM and IEEE. (Visit: www.cerias.purdue.edu)
Views: 1574 ceriaspurdue
KDD 2009 (2)
 
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Views: 239 Jessyhonda7
What future for Big Data mining?
 
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Policymakers are showing growing interest for real-time analysis of public opinion and Big Data. From finance to political campaigners, social media have become a primary source of information, especially when it comes to understanding public opinion trends. However, the potential of social media still needs to be fully exploited. With the explosion of structured and unstructured Big Data, the ability to harness information has become paramount for those who want to successfully use information originating from social media. On the regulatory side, the European Commission wants to promote the data-driven economy as part of its Digital Single Market strategy. The strategy includes better online access and digitalisation as a driver for growth.
Views: 783 SSIX Project
Intrusion Detection Technique by using K means, Fuzzy Neural Network and SVM classifiers
 
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To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, 45, KAMARAJ SALAI, THATTANCHAVADY, PUDUCHERRY-9 Landmark: Opposite to Thattanchavady Industrial Estate, Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Intrusion Detection Technique by using K-means, Fuzzy Neural Network and SVM classifiers With the impending era of internet, the network security has become the key foundation for lot of financial and business web applications. Intrusion detection is one of the looms to resolve the problem of network security. Imperfectness of intrusion detection systems (IDS) has given an opportunity for data mining to make several important contributions to the field of intrusion detection. In recent years, many researchers are using data mining techniques for building IDS. Here, we propose a new approach by utilizing data mining techniques such as neuro-fuzzy and radial basis support vector machine (SVM) for helping IDS to attain higher detection rate. The proposed technique has four major steps: primarily, k-means clustering is used to generate different training subsets. Then, based on the obtained training subsets, different neuro-fuzzy models are trained. Subsequently, a vector for SVM classification is formed and in the end, classification using radial SVM is performed to detect intrusion has happened or not. To illustrate the applicability and capability of the new approach, the results of experiments on KDD CUP 1999 dataset is demonstrated. Experimental results shows that our proposed new approach do better than BPNN, multiclass SVM and other well-known methods such as decision trees and Columbia model in terms of sensitivity, specificity and in particular detection accuracy.
Views: 8054 jpinfotechprojects
An Internal Intrusion Detection and Protection System - Data Mining and Forensic Techniques
 
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Including Packages ======================= * Base Paper * 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: 559 Clickmyproject
Data Mining with Weka (2.2: Training and testing)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Training and testing http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 68565 WekaMOOC
Paper Data Mining for Network Intrusion Detection
 
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كةمبيني بةكوردي كردني زانست لة زانكؤي كةشةبيَداني مرؤيي
Views: 166 Hawnaz Mustafa
VC-Dimension and Rademacher Averages - Part 1
 
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Author: Matteo Riondato, Eli Upfal Abstract: Rademacher Averages and the Vapnik-Chervonenkis dimension are fundamental concepts from statistical learning theory. They allow to study simultaneous deviation bounds of empirical averages from their expectations for classes of functions, by considering properties of the functions, of their domain (the dataset), and of the sampling process. In this tutorial, we survey the use of Rademacher Averages and the VC-dimension in sampling-based algorithms for graph analysis and pattern mining. We start from their theoretical foundations at the core of machine learning, then show a generic recipe for formulating data mining problems in a way that allows to use these concepts in efficient randomized algorithms for those problems. Finally, we show examples of the application of the recipe to graph problems (connectivity, shortest paths, betweenness centrality) and pattern mining. Our goal is to expose the usefulness of these techniques for the data mining researcher, and to encourage research in the area. ACM DL: http://dl.acm.org/citation.cfm?id=2789984 DOI: http://dx.doi.org/10.1145/2783258.2789984
kmeans using weka
 
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kmeans example on weka.
Collaborative Innovation: Algorithm/Big Data Challenges
 
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At the 2012 Collaborative Innovation summit on public prizes and challenges, Jennifer Fogarty, Space Life Sciences Innovation Lead at NASA moderates a panel discussion on Algorithm/Big Data Challenges featuring; Jason Crusan, Director, NASA Center of Excellence for Collaborative Innovation —NASA Tournament Lab; Greg Hanson, Chief Operations Officer and General Manager, Tree.com — Lending Tree Challenges; Balaji Krishnapuram, Senior Staff Scientist,Seimens Medical Solutions, USA — Seimens Mining Medical Data / KDD Cup; Jonathan Gluck, Heritage Provider Network — Heritage Health Prize.
Views: 306 casefoundation

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