Intrusion Detection. Jeffrey J.P. Tsai. Imperial College Press. A Machine Learning Approach. Zhenwei Yu. University of Illinois, Chicago, USA

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Intrusion Detection. Jeffrey J.P. Tsai. Imperial College Press. A Machine Learning Approach. Zhenwei Yu. University of Illinois, Chicago, USA"

Transcription

1 SERIES IN ELECTRICAL AND COMPUTER ENGINEERING Intrusion Detection A Machine Learning Approach Zhenwei Yu University of Illinois, Chicago, USA Jeffrey J.P. Tsai Asia University, University of Illinois, Taiwan Chicago, USA Imperial College Press

2 Contents Preface vii 1. Introduction Background 1.2 Existing Problems Alarm management Performance maintenance Attacks and Countermeasures in Computer Security General Security Objectives Accountability Assurance Authentication Authorization Availability Confidentiality Integrity Non-repudiation Types of Attacks Attacks against availability Attacks against confidentiality Attacks against integrity Attacks against miscellaneous security objectives Countermeasures of Attacks Authentication Access control Audit and intrusion detection 20 ix

3 x Intrusion Detection: A Machine Learning Approach Extrusion detection Cryptography Firewall Anti-virus software Machine Learning Methods Background Concept Learning Decision Tree Neural Networks Bayesian Learning Genetic Algorithms and Genetic Programming Instance-Based Learning Inductive Logic Programming Analytical Learning Inductive and Analytical Learning Reinforcement Learning Ensemble Learning Multiple Instance Learning Unsupervised Learning Semi-Supervised Learning Support Vector Machines Intrusion Detection System Background Security defense in depth A brief history of intrusion detection Classification of intrusion detection system Standardization efforts General model of intrusion detection system Available Audit Data System features User activities Network activities Preprocess Methods Detection Methods Statistical analysis Expert system 51

4 Contents x\ Model-based system State transition-based analysis Neural network-based system Data mining-based system Architecture for Network Intrusion Detection System 56 Part A: Intrusion Detection for Wired Network 5. Techniques for Intrusion Detection Available Alarm Management Solutions Alarm correlation Alarm filter Event classification process Available Performance Maintenance Solutions Adaptive learning Incremental mining Adaptive Automatically Tuning Intrusion Detection System Architecture SOM-Based Labeling Tool Training algorithm Pre-cluster by symbolic features Cluster by SOM Label data in clusters Hybrid Detection Model Binary SLIPPER rule learning system Binary classifiers Final arbiter Detection model tuning Fuzzy prediction filter Fuzzy tuning controller System Prototype and Performance Evaluation Implementation of Prototype Fuzzy controller Binary prediction and model tuning thread Final arbiter and prediction filter thread 102

5 xjj Intrusion Detection: A Machine Learning Approach User simulator thread Interface for fuzzy knowledge base Experimental Data set and Related Systems KDDCup'99 intrusion detection data set Performance evaluation method Related IDSs on KDDCup'99 ID data set Performance Evaluation SOM-based labeling tool performance Build hybrid detection model The MC-SLIPPER system and test performance The ATIDS system and test performance The ADAT IDS system and test performance Part B: Intrusion Detection for Wireless Sensor Network 8. Attacks against Wireless Sensor Network Wireless Sensor Network Challenges on Intrusion Detection in WSNs Attacks against WSNs Intrusion Detection System for Wireless Sensor Network Architecture of IDS for WSN Audit Data in WSN Local features for LIDC in WSN Packet features for PIDC in WSN Detection Model and Optimization Model Tuning Conclusion and Future Research 157 Cited Literature 159 Index 169

Detection. Perspective. Network Anomaly. Bhattacharyya. Jugal. A Machine Learning »C) Dhruba Kumar. Kumar KaKta. CRC Press J Taylor & Francis Croup

Detection. Perspective. Network Anomaly. Bhattacharyya. Jugal. A Machine Learning »C) Dhruba Kumar. Kumar KaKta. CRC Press J Taylor & Francis Croup Network Anomaly Detection A Machine Learning Perspective Dhruba Kumar Bhattacharyya Jugal Kumar KaKta»C) CRC Press J Taylor & Francis Croup Boca Raton London New York CRC Press is an imprint of the Taylor

More information

Network Machine Learning Research Group. Intended status: Informational October 19, 2015 Expires: April 21, 2016

Network Machine Learning Research Group. Intended status: Informational October 19, 2015 Expires: April 21, 2016 Network Machine Learning Research Group S. Jiang Internet-Draft Huawei Technologies Co., Ltd Intended status: Informational October 19, 2015 Expires: April 21, 2016 Abstract Network Machine Learning draft-jiang-nmlrg-network-machine-learning-00

More information

The Integration of SNORT with K-Means Clustering Algorithm to Detect New Attack

The Integration of SNORT with K-Means Clustering Algorithm to Detect New Attack The Integration of SNORT with K-Means Clustering Algorithm to Detect New Attack Asnita Hashim, University of Technology MARA, Malaysia April 14-15, 2011 The Integration of SNORT with K-Means Clustering

More information

A Review of Anomaly Detection Techniques in Network Intrusion Detection System

A Review of Anomaly Detection Techniques in Network Intrusion Detection System A Review of Anomaly Detection Techniques in Network Intrusion Detection System Dr.D.V.S.S.Subrahmanyam Professor, Dept. of CSE, Sreyas Institute of Engineering & Technology, Hyderabad, India ABSTRACT:In

More information

Federico Rajola. Customer Relationship. Management in the. Financial Industry. Organizational Processes and. Technology Innovation.

Federico Rajola. Customer Relationship. Management in the. Financial Industry. Organizational Processes and. Technology Innovation. Federico Rajola Customer Relationship Management in the Financial Industry Organizational Processes and Technology Innovation Second edition ^ Springer Contents 1 Introduction 1 1.1 Identification and

More information

Intrusion Detection for Mobile Ad Hoc Networks

Intrusion Detection for Mobile Ad Hoc Networks Intrusion Detection for Mobile Ad Hoc Networks Tom Chen SMU, Dept of Electrical Engineering tchen@engr.smu.edu http://www.engr.smu.edu/~tchen TC/Rockwell/5-20-04 SMU Engineering p. 1 Outline Security problems

More information

Data Mining for Network Intrusion Detection

Data Mining for Network Intrusion Detection Data Mining for Network Intrusion Detection S Terry Brugger UC Davis Department of Computer Science Data Mining for Network Intrusion Detection p.1/55 Overview This is important for defense in depth Much

More information

KEITH LEHNERT AND ERIC FRIEDRICH

KEITH LEHNERT AND ERIC FRIEDRICH MACHINE LEARNING CLASSIFICATION OF MALICIOUS NETWORK TRAFFIC KEITH LEHNERT AND ERIC FRIEDRICH 1. Introduction 1.1. Intrusion Detection Systems. In our society, information systems are everywhere. They

More information

Network Intrusion Detection and Prevention

Network Intrusion Detection and Prevention Ali A. Ghorbani Wei Lu Mahbod Tavallaee Network Intrusion Detection and Prevention Concepts and Techniques )Spri inger Contents 1 Network Attacks 1 1.1 Attack Taxonomies 2 1.2 Probes 4 1.2.1 IPSweep and

More information

Data Mining Part 5. Prediction

Data Mining Part 5. Prediction Data Mining Part 5. Prediction 5.1 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Classification vs. Numeric Prediction Prediction Process Data Preparation Comparing Prediction Methods References Classification

More information

WINSOME: a Middleware Platform for the Provision of Secure Monitoring Services over Wireless Sensor Networks

WINSOME: a Middleware Platform for the Provision of Secure Monitoring Services over Wireless Sensor Networks WINSOME: a Middleware Platform for the Provision of Secure Monitoring Services over Wireless Sensor Networks L. Pomante, M. Pugliese, S. Marchesani, F. Santucci University of L Aquila ITALY Center of Excellence

More information

Data Mining for Customer Service Support. Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin

Data Mining for Customer Service Support. Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin Data Mining for Customer Service Support Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin Traditional Hotline Services Problem Traditional Customer Service Support (manufacturing)

More information

Application of Data Mining Techniques in Intrusion Detection

Application of Data Mining Techniques in Intrusion Detection Application of Data Mining Techniques in Intrusion Detection LI Min An Yang Institute of Technology leiminxuan@sohu.com Abstract: The article introduced the importance of intrusion detection, as well as

More information

Master of Science in Computer Science

Master of Science in Computer Science Master of Science in Computer Science Background/Rationale The MSCS program aims to provide both breadth and depth of knowledge in the concepts and techniques related to the theory, design, implementation,

More information

Learning is a very general term denoting the way in which agents:

Learning is a very general term denoting the way in which agents: What is learning? Learning is a very general term denoting the way in which agents: Acquire and organize knowledge (by building, modifying and organizing internal representations of some external reality);

More information

Welcome. Data Mining: Updates in Technologies. Xindong Wu. Colorado School of Mines Golden, Colorado 80401, USA

Welcome. Data Mining: Updates in Technologies. Xindong Wu. Colorado School of Mines Golden, Colorado 80401, USA Welcome Xindong Wu Data Mining: Updates in Technologies Dept of Math and Computer Science Colorado School of Mines Golden, Colorado 80401, USA Email: xwu@ mines.edu Home Page: http://kais.mines.edu/~xwu/

More information

Hybrid Model For Intrusion Detection System Chapke Prajkta P., Raut A. B.

Hybrid Model For Intrusion Detection System Chapke Prajkta P., Raut A. B. www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume1 Issue 3 Dec 2012 Page No. 151-155 Hybrid Model For Intrusion Detection System Chapke Prajkta P., Raut A. B.

More information

TIME SCHEDULE. 1 Introduction to Computer Security & Cryptography 13

TIME SCHEDULE. 1 Introduction to Computer Security & Cryptography 13 COURSE TITLE : INFORMATION SECURITY COURSE CODE : 5136 COURSE CATEGORY : ELECTIVE PERIODS/WEEK : 4 PERIODS/SEMESTER : 52 CREDITS : 4 TIME SCHEDULE MODULE TOPICS PERIODS 1 Introduction to Computer Security

More information

Intrusion Detection: Game Theory, Stochastic Processes and Data Mining

Intrusion Detection: Game Theory, Stochastic Processes and Data Mining Intrusion Detection: Game Theory, Stochastic Processes and Data Mining Joseph Spring 7COM1028 Secure Systems Programming 1 Discussion Points Introduction Firewalls Intrusion Detection Schemes Models Stochastic

More information

Practical Applications of DATA MINING. Sang C Suh Texas A&M University Commerce JONES & BARTLETT LEARNING

Practical Applications of DATA MINING. Sang C Suh Texas A&M University Commerce JONES & BARTLETT LEARNING Practical Applications of DATA MINING Sang C Suh Texas A&M University Commerce r 3 JONES & BARTLETT LEARNING Contents Preface xi Foreword by Murat M.Tanik xvii Foreword by John Kocur xix Chapter 1 Introduction

More information

Machine Learning: Overview

Machine Learning: Overview Machine Learning: Overview Why Learning? Learning is a core of property of being intelligent. Hence Machine learning is a core subarea of Artificial Intelligence. There is a need for programs to behave

More information

A Review of Data Mining based Intrusion Detection Techniques

A Review of Data Mining based Intrusion Detection Techniques A Review of Data Mining based Intrusion Detection Techniques Kamini Maheshwar 1 and Divakar Singh 2 1,2 Department of CSE BUIT, Barkatullah University Bhopal, (M.P), India ABSTRACT Traditional Data Mining

More information

Industrial Network Security for SCADA, Automation, Process Control and PLC Systems. Contents. 1 An Introduction to Industrial Network Security 1

Industrial Network Security for SCADA, Automation, Process Control and PLC Systems. Contents. 1 An Introduction to Industrial Network Security 1 Industrial Network Security for SCADA, Automation, Process Control and PLC Systems Contents 1 An Introduction to Industrial Network Security 1 1.1 Course overview 1 1.2 The evolution of networking 1 1.3

More information

Applying Data Mining of Fuzzy Association Rules to Network Intrusion Detection

Applying Data Mining of Fuzzy Association Rules to Network Intrusion Detection Applying Data Mining of Fuzzy Association Rules to Network Intrusion Detection Authors: Aly El-Semary, Janica Edmonds, Jesús González-Pino, and Mauricio Papa Center for Information Security Department

More information

Performance Evaluation of Intrusion Detection Systems

Performance Evaluation of Intrusion Detection Systems Performance Evaluation of Intrusion Detection Systems Waleed Farag & Sanwar Ali Department of Computer Science at Indiana University of Pennsylvania ABIT 2006 Outline Introduction: Intrusion Detection

More information

Observation and Findings

Observation and Findings Chapter 6 Observation and Findings 6.1. Introduction This chapter discuss in detail about observation and findings based on survey performed. This research work is carried out in order to find out network

More information

Master s Program in Information Systems

Master s Program in Information Systems The University of Jordan King Abdullah II School for Information Technology Department of Information Systems Master s Program in Information Systems 2006/2007 Study Plan Master Degree in Information Systems

More information

SURVEY OF INTRUSION DETECTION SYSTEM

SURVEY OF INTRUSION DETECTION SYSTEM SURVEY OF INTRUSION DETECTION SYSTEM PRAJAPATI VAIBHAVI S. SHARMA DIPIKA V. ASST. PROF. ASST. PROF. MANISH INSTITUTE OF COMPUTER STUDIES MANISH INSTITUTE OF COMPUTER STUDIES VISNAGAR VISNAGAR GUJARAT GUJARAT

More information

COMPUTER ENGINEERING GRADUTE PROGRAM FOR MASTER S DEGREE (With Thesis)

COMPUTER ENGINEERING GRADUTE PROGRAM FOR MASTER S DEGREE (With Thesis) COMPUTER ENGINEERING GRADUTE PROGRAM FOR MASTER S DEGREE (With Thesis) PREPARATORY PROGRAM* COME 27 Advanced Object Oriented Programming 5 COME 21 Data Structures and Algorithms COME 22 COME 1 COME 1 COME

More information

Introduction to Cyber Security / Information Security

Introduction to Cyber Security / Information Security Introduction to Cyber Security / Information Security Syllabus for Introduction to Cyber Security / Information Security program * for students of University of Pune is given below. The program will be

More information

INTRUSION DETECTION SYSTEM ON MOBILE AD HOC NETWORK

INTRUSION DETECTION SYSTEM ON MOBILE AD HOC NETWORK INTRUSION DETECTION SYSTEM ON MOBILE AD HOC NETWORK Kruahnadeo Belerao M.E. student JSPM Imperial College Of Engg. Wagholi,Pune Vinod Wadane M.E. student JSPM Imperial College Of Engg. Wagholi,Pune ABSTRACT

More information

A SURVEY ON GENETIC ALGORITHM FOR INTRUSION DETECTION SYSTEM

A SURVEY ON GENETIC ALGORITHM FOR INTRUSION DETECTION SYSTEM A SURVEY ON GENETIC ALGORITHM FOR INTRUSION DETECTION SYSTEM MS. DIMPI K PATEL Department of Computer Science and Engineering, Hasmukh Goswami college of Engineering, Ahmedabad, Gujarat ABSTRACT The Internet

More information

Company Co. Inc. LLC. LAN Domain Network Security Best Practices. An integrated approach to securing Company Co. Inc.

Company Co. Inc. LLC. LAN Domain Network Security Best Practices. An integrated approach to securing Company Co. Inc. Company Co. Inc. LLC Multiple Minds, Singular Results LAN Domain Network Security Best Practices An integrated approach to securing Company Co. Inc. LLC s network Written and Approved By: Geoff Lacy, Tim

More information

NETWORK INTRUSION DETECTION SYSTEM USING HYBRID CLASSIFICATION MODEL

NETWORK INTRUSION DETECTION SYSTEM USING HYBRID CLASSIFICATION MODEL NETWORK INTRUSION DETECTION SYSTEM USING HYBRID CLASSIFICATION MODEL Prof. Santosh T. Waghmode 1, Prof. Vinod S. Wadne 2 Department of Computer Engineering, 1, 2 JSPM s Imperial College of Engineering

More information

Development of a Network Intrusion Detection System

Development of a Network Intrusion Detection System Development of a Network Intrusion Detection System (I): Agent-based Design (FLC1) (ii): Detection Algorithm (FLC2) Supervisor: Dr. Korris Chung Please visit my personal homepage www.comp.polyu.edu.hk/~cskchung/fyp04-05/

More information

Security Issues in SCADA Networks

Security Issues in SCADA Networks Security Issues in SCADA Networks by V. M. Igure, S. A. Laughter, and R. D. Williams Computers & Security, 25(7): 498-506, 2006 presented by Ruilong Deng Postdoctoral Research Fellow School of Electrical

More information

Machine Learning. Chapter 18, 21. Some material adopted from notes by Chuck Dyer

Machine Learning. Chapter 18, 21. Some material adopted from notes by Chuck Dyer Machine Learning Chapter 18, 21 Some material adopted from notes by Chuck Dyer What is learning? Learning denotes changes in a system that... enable a system to do the same task more efficiently the next

More information

CHAPTER 1 INTRODUCTION

CHAPTER 1 INTRODUCTION 21 CHAPTER 1 INTRODUCTION 1.1 PREAMBLE Wireless ad-hoc network is an autonomous system of wireless nodes connected by wireless links. Wireless ad-hoc network provides a communication over the shared wireless

More information

Mining. Practical. Data. Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press. Taylor & Francis Group

Mining. Practical. Data. Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press. Taylor & Francis Group Practical Data Mining Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor Ei Francis Group, an Informs

More information

Data Mining For Intrusion Detection Systems. Monique Wooten. Professor Robila

Data Mining For Intrusion Detection Systems. Monique Wooten. Professor Robila Data Mining For Intrusion Detection Systems Monique Wooten Professor Robila December 15, 2008 Wooten 2 ABSTRACT The paper discusses the use of data mining techniques applied to intrusion detection systems.

More information

Doctor of Philosophy in Computer Science

Doctor of Philosophy in Computer Science Doctor of Philosophy in Computer Science Background/Rationale The program aims to develop computer scientists who are armed with methods, tools and techniques from both theoretical and systems aspects

More information

Implementation of Intelligent Techniques for Intrusion Detection Systems

Implementation of Intelligent Techniques for Intrusion Detection Systems Ain Shams University Faculty of Computer & Information Sciences Implementation of Intelligent Techniques for Intrusion Detection Systems A Thesis Submitted to Department of Computer Science In partial

More information

A survey on Data Mining based Intrusion Detection Systems

A survey on Data Mining based Intrusion Detection Systems International Journal of Computer Networks and Communications Security VOL. 2, NO. 12, DECEMBER 2014, 485 490 Available online at: www.ijcncs.org ISSN 2308-9830 A survey on Data Mining based Intrusion

More information

A Survey on Intrusion Detection using Data Mining Technique

A Survey on Intrusion Detection using Data Mining Technique A Survey on Intrusion Detection using Data Mining Technique D. Shona, A.Shobana Assistant Professor, Dept. of Computer Science, Sri Krishna Arts & Science College, Coimbatore, India 1 M.Phil. Scholar,

More information

A Hybrid Intrusion Detection System of Cluster-based Wireless Sensor Networks

A Hybrid Intrusion Detection System of Cluster-based Wireless Sensor Networks A Hybrid Intrusion Detection System of Cluster-based Wireless Sensor Networks K.Q. Yan, S.C. Wang, C.W. Liu Abstract Recent advances in Wireless Sensor Networks (WSNs) have made them extremely useful in

More information

Contents. Dedication List of Figures List of Tables. Acknowledgments

Contents. Dedication List of Figures List of Tables. Acknowledgments Contents Dedication List of Figures List of Tables Foreword Preface Acknowledgments v xiii xvii xix xxi xxv Part I Concepts and Techniques 1. INTRODUCTION 3 1 The Quest for Knowledge 3 2 Problem Description

More information

A Model-based Methodology for Developing Secure VoIP Systems

A Model-based Methodology for Developing Secure VoIP Systems A Model-based Methodology for Developing Secure VoIP Systems Juan C Pelaez, Ph. D. November 24, 200 VoIP overview What is VoIP? Why use VoIP? Strong effect on global communications VoIP will replace PSTN

More information

CONTENTS PREFACE 1 INTRODUCTION 1 2 DATA VISUALIZATION 19

CONTENTS PREFACE 1 INTRODUCTION 1 2 DATA VISUALIZATION 19 PREFACE xi 1 INTRODUCTION 1 1.1 Overview 1 1.2 Definition 1 1.3 Preparation 2 1.3.1 Overview 2 1.3.2 Accessing Tabular Data 3 1.3.3 Accessing Unstructured Data 3 1.3.4 Understanding the Variables and Observations

More information

A Partially Supervised Metric Multidimensional Scaling Algorithm for Textual Data Visualization

A Partially Supervised Metric Multidimensional Scaling Algorithm for Textual Data Visualization A Partially Supervised Metric Multidimensional Scaling Algorithm for Textual Data Visualization Ángela Blanco Universidad Pontificia de Salamanca ablancogo@upsa.es Spain Manuel Martín-Merino Universidad

More information

Efficient Security Alert Management System

Efficient Security Alert Management System Efficient Security Alert Management System Minoo Deljavan Anvary IT Department School of e-learning Shiraz University Shiraz, Fars, Iran Majid Ghonji Feshki Department of Computer Science Qzvin Branch,

More information

Weighted Total Mark. Weighted Exam Mark

Weighted Total Mark. Weighted Exam Mark CMP4103 Computer Systems and Network Security Period per Week Contact Hour per Semester Weighted Total Mark Weighted Exam Mark Weighted Continuous Assessment Mark Credit Units LH PH TH CH WTM WEM WCM CU

More information

Data Mining and Neural Networks in Stata

Data Mining and Neural Networks in Stata Data Mining and Neural Networks in Stata 2 nd Italian Stata Users Group Meeting Milano, 10 October 2005 Mario Lucchini e Maurizo Pisati Università di Milano-Bicocca mario.lucchini@unimib.it maurizio.pisati@unimib.it

More information

Layered Approach of Intrusion Detection System with Efficient Alert Aggregation for Heterogeneous Networks

Layered Approach of Intrusion Detection System with Efficient Alert Aggregation for Heterogeneous Networks Layered Approach of Intrusion Detection System with Efficient Alert Aggregation for Heterogeneous Networks Lohith Raj S N, Shanthi M B, Jitendranath Mungara Abstract Protecting data from the intruders

More information

Review Article Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks

Review Article Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks Distributed Sensor Networks, Article ID 351047, 6 pages http://dx.doi.org/10.1155/2013/351047 Review Article Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks

More information

Data Mining: Concepts and Techniques. Jiawei Han. Micheline Kamber. Simon Fräser University К MORGAN KAUFMANN PUBLISHERS. AN IMPRINT OF Elsevier

Data Mining: Concepts and Techniques. Jiawei Han. Micheline Kamber. Simon Fräser University К MORGAN KAUFMANN PUBLISHERS. AN IMPRINT OF Elsevier Data Mining: Concepts and Techniques Jiawei Han Micheline Kamber Simon Fräser University К MORGAN KAUFMANN PUBLISHERS AN IMPRINT OF Elsevier Contents Foreword Preface xix vii Chapter I Introduction I I.

More information

Hybrid Intrusion Detection System Model using Clustering, Classification and Decision Table

Hybrid Intrusion Detection System Model using Clustering, Classification and Decision Table IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661, p- ISSN: 2278-8727Volume 9, Issue 4 (Mar. - Apr. 2013), PP 103-107 Hybrid Intrusion Detection System Model using Clustering, Classification

More information

A Survey on Intrusion Detection System with Data Mining Techniques

A Survey on Intrusion Detection System with Data Mining Techniques A Survey on Intrusion Detection System with Data Mining Techniques Ms. Ruth D 1, Mrs. Lovelin Ponn Felciah M 2 1 M.Phil Scholar, Department of Computer Science, Bishop Heber College (Autonomous), Trichirappalli,

More information

An Anomaly-Based Method for DDoS Attacks Detection using RBF Neural Networks

An Anomaly-Based Method for DDoS Attacks Detection using RBF Neural Networks 2011 International Conference on Network and Electronics Engineering IPCSIT vol.11 (2011) (2011) IACSIT Press, Singapore An Anomaly-Based Method for DDoS Attacks Detection using RBF Neural Networks Reyhaneh

More information

A Clustering based Intrusion Detection System for Storage Area Network

A Clustering based Intrusion Detection System for Storage Area Network A Clustering based Detection System for Storage Area Network Garima Singh Anubhav Patrick Lucky Rajpoot ABSTRACT A storage area network (SAN) is a high-speed widely used special-purpose network that interconnects

More information

CNA 432/532 OSI Layers Security

CNA 432/532 OSI Layers Security CNA 432/532 OSI Layers Location: ECC 116 Days: Thursday Semester: Fall 2012 Times: 5:00-7:50 pm Professor: Dr. Amos Olagunju E-mail: aoolagunju@stcloudstate.edu Office Hrs: 3-4 MW, Office: ECC256 Other

More information

01219211 Software Development Training Camp 1 (0-3) Prerequisite : 01204214 Program development skill enhancement camp, at least 48 person-hours.

01219211 Software Development Training Camp 1 (0-3) Prerequisite : 01204214 Program development skill enhancement camp, at least 48 person-hours. (International Program) 01219141 Object-Oriented Modeling and Programming 3 (3-0) Object concepts, object-oriented design and analysis, object-oriented analysis relating to developing conceptual models

More information

Master of Science in Health Information Technology Degree Curriculum

Master of Science in Health Information Technology Degree Curriculum Master of Science in Health Information Technology Degree Curriculum Core courses: 8 courses Total Credit from Core Courses = 24 Core Courses Course Name HRS Pre-Req Choose MIS 525 or CIS 564: 1 MIS 525

More information

Network Intrusion Detection Systems

Network Intrusion Detection Systems Network Intrusion Detection Systems False Positive Reduction Through Anomaly Detection Joint research by Emmanuele Zambon & Damiano Bolzoni 7/1/06 NIDS - False Positive reduction through Anomaly Detection

More information

Introduction to Machine Learning Lecture 1. Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu

Introduction to Machine Learning Lecture 1. Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu Introduction to Machine Learning Lecture 1 Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu Introduction Logistics Prerequisites: basics concepts needed in probability and statistics

More information

Data Mining. 1 Introduction 2 Data Mining methods. Alfred Holl Data Mining 1

Data Mining. 1 Introduction 2 Data Mining methods. Alfred Holl Data Mining 1 Data Mining 1 Introduction 2 Data Mining methods Alfred Holl Data Mining 1 1 Introduction 1.1 Motivation 1.2 Goals and problems 1.3 Definitions 1.4 Roots 1.5 Data Mining process 1.6 Epistemological constraints

More information

Developing Network Security Strategies

Developing Network Security Strategies NETE-4635 Computer Network Analysis and Design Developing Network Security Strategies NETE4635 - Computer Network Analysis and Design Slide 1 Network Security Design The 12 Step Program 1. Identify network

More information

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014 RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer

More information

HYBRID INTRUSION DETECTION FOR CLUSTER BASED WIRELESS SENSOR NETWORK

HYBRID INTRUSION DETECTION FOR CLUSTER BASED WIRELESS SENSOR NETWORK HYBRID INTRUSION DETECTION FOR CLUSTER BASED WIRELESS SENSOR NETWORK 1 K.RANJITH SINGH 1 Dept. of Computer Science, Periyar University, TamilNadu, India 2 T.HEMA 2 Dept. of Computer Science, Periyar University,

More information

Rajan R. Pant Controller Office of Controller of Certification Ministry of Science & Technology rajan@cca.gov.np

Rajan R. Pant Controller Office of Controller of Certification Ministry of Science & Technology rajan@cca.gov.np Rajan R. Pant Controller Office of Controller of Certification Ministry of Science & Technology rajan@cca.gov.np Meaning Why is Security Audit Important Framework Audit Process Auditing Application Security

More information

INTRUSION DETECTION SYSTEMS and Network Security

INTRUSION DETECTION SYSTEMS and Network Security INTRUSION DETECTION SYSTEMS and Network Security Intrusion Detection System IDS A layered network security approach starts with : A well secured system which starts with: Up-to-date application and OS

More information

Integration Misuse and Anomaly Detection Techniques on Distributed Sensors

Integration Misuse and Anomaly Detection Techniques on Distributed Sensors Integration Misuse and Anomaly Detection Techniques on Distributed Sensors Shih-Yi Tu Chung-Huang Yang Kouichi Sakurai Graduate Institute of Information and Computer Education, National Kaohsiung Normal

More information

FUZZY DATA MINING AND GENETIC ALGORITHMS APPLIED TO INTRUSION DETECTION

FUZZY DATA MINING AND GENETIC ALGORITHMS APPLIED TO INTRUSION DETECTION FUZZY DATA MINING AND GENETIC ALGORITHMS APPLIED TO INTRUSION DETECTION Susan M. Bridges Bridges@cs.msstate.edu Rayford B. Vaughn vaughn@cs.msstate.edu 23 rd National Information Systems Security Conference

More information

STANDARDISATION AND CLASSIFICATION OF ALERTS GENERATED BY INTRUSION DETECTION SYSTEMS

STANDARDISATION AND CLASSIFICATION OF ALERTS GENERATED BY INTRUSION DETECTION SYSTEMS STANDARDISATION AND CLASSIFICATION OF ALERTS GENERATED BY INTRUSION DETECTION SYSTEMS Athira A B 1 and Vinod Pathari 2 1 Department of Computer Engineering,National Institute Of Technology Calicut, India

More information

SOME CLUSTERING ALGORITHMS TO ENHANCE THE PERFORMANCE OF THE NETWORK INTRUSION DETECTION SYSTEM

SOME CLUSTERING ALGORITHMS TO ENHANCE THE PERFORMANCE OF THE NETWORK INTRUSION DETECTION SYSTEM SOME CLUSTERING ALGORITHMS TO ENHANCE THE PERFORMANCE OF THE NETWORK INTRUSION DETECTION SYSTEM Mrutyunjaya Panda, 2 Manas Ranjan Patra Department of E&TC Engineering, GIET, Gunupur, India 2 Department

More information

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 Introduction to Machine Learning Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 1 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning

More information

Course Content Summary ITN 262 Network Communication, Security and Authentication (4 Credits)

Course Content Summary ITN 262 Network Communication, Security and Authentication (4 Credits) Page 1 of 5 Course Content Summary ITN 262 Network Communication, Security and Authentication (4 Credits) TNCC Cybersecurity Program web page: http://tncc.edu/programs/cyber-security Course Description:

More information

Information Technology Career Cluster Introduction to Cybersecurity Course Number: 11.48100

Information Technology Career Cluster Introduction to Cybersecurity Course Number: 11.48100 Information Technology Career Cluster Introduction to Cybersecurity Course Number: 11.48100 Course Description: Introduction to Cybersecurity is designed to provide students the basic concepts and terminology

More information

IDS / IPS. James E. Thiel S.W.A.T.

IDS / IPS. James E. Thiel S.W.A.T. IDS / IPS An introduction to intrusion detection and intrusion prevention systems James E. Thiel January 14, 2005 S.W.A.T. Drexel University Overview Intrusion Detection Purpose Types Detection Methods

More information

CSCE 465 Computer & Network Security

CSCE 465 Computer & Network Security CSCE 465 Computer & Network Security Instructor: Dr. Guofei Gu http://courses.cse.tamu.edu/guofei/csce465/ Intrusion Detection System 1 Intrusion Definitions A set of actions aimed to compromise the security

More information

Azure Machine Learning, SQL Data Mining and R

Azure Machine Learning, SQL Data Mining and R Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:

More information

An analysis of suitable parameters for efficiently applying K-means clustering to large TCPdump data set using Hadoop framework

An analysis of suitable parameters for efficiently applying K-means clustering to large TCPdump data set using Hadoop framework An analysis of suitable parameters for efficiently applying K-means clustering to large TCPdump data set using Hadoop framework Jakrarin Therdphapiyanak Dept. of Computer Engineering Chulalongkorn University

More information

Network Intrusion Detection using Semi Supervised Support Vector Machine

Network Intrusion Detection using Semi Supervised Support Vector Machine Network Intrusion Detection using Semi Supervised Support Vector Machine Jyoti Haweliya Department of Computer Engineering Institute of Engineering & Technology, Devi Ahilya University Indore, India ABSTRACT

More information

DATA MINING USING INTEGRATION OF CLUSTERING AND DECISION TREE

DATA MINING USING INTEGRATION OF CLUSTERING AND DECISION TREE DATA MINING USING INTEGRATION OF CLUSTERING AND DECISION TREE 1 K.Murugan, 2 P.Varalakshmi, 3 R.Nandha Kumar, 4 S.Boobalan 1 Teaching Fellow, Department of Computer Technology, Anna University 2 Assistant

More information

International Journal of Computer Science and Applications Vol. 6, No. 3, pp 20 32, 2009

International Journal of Computer Science and Applications Vol. 6, No. 3, pp 20 32, 2009 International Journal of Computer Science and Applications Vol. 6, No. 3, pp 20 32, 2009 Technomathematics Research Foundation ATTACK CLASSIFICATION BASED ON DATA MINING TECHNIQUE AND ITS APPLICATION FOR

More information

A Novel Solution on Alert Conflict Resolution Model in Network Management

A Novel Solution on Alert Conflict Resolution Model in Network Management A Novel Solution on Alert Conflict Resolution Model in Network Management Yi-Tung F. Chan University of Wales United Kingdom FrankChan2005@gmail.com Ramaswamy D.Thiyagu University of East London United

More information

DATA MINING TECHNIQUES AND APPLICATIONS

DATA MINING TECHNIQUES AND APPLICATIONS DATA MINING TECHNIQUES AND APPLICATIONS Mrs. Bharati M. Ramageri, Lecturer Modern Institute of Information Technology and Research, Department of Computer Application, Yamunanagar, Nigdi Pune, Maharashtra,

More information

Intrusion Detection System using Log Files and Reinforcement Learning

Intrusion Detection System using Log Files and Reinforcement Learning Intrusion Detection System using Log Files and Reinforcement Learning Bhagyashree Deokar, Ambarish Hazarnis Department of Computer Engineering K. J. Somaiya College of Engineering, Mumbai, India ABSTRACT

More information

A Technical Review on Intrusion Detection System

A Technical Review on Intrusion Detection System A Technical Review on Intrusion Detection System Sejal K. Patel Umang H. Mehta Urmi M. Patel Dhruv H. Bhagat Pratik Nayak Teaching Assistant at department of computer science and technology Ankita D. Patel

More information

CSE 5392 Sensor Network Security

CSE 5392 Sensor Network Security About Instructor CSE 5392 Sensor Network Security Course Introduction Dr. Donggang Liu, assistant professor, CSE department http://ranger.uta.edu/~dliu dliu@cse.uta.edu Tel: (817) 272-0741 Office: 330NH

More information

Data Mining and Statistics for Decision Making. Wiley Series in Computational Statistics

Data Mining and Statistics for Decision Making. Wiley Series in Computational Statistics Brochure More information from http://www.researchandmarkets.com/reports/2171080/ Data Mining and Statistics for Decision Making. Wiley Series in Computational Statistics Description: Data Mining and Statistics

More information

Essential Components of an Integrated Data Mining Tool for the Oil & Gas Industry, With an Example Application in the DJ Basin.

Essential Components of an Integrated Data Mining Tool for the Oil & Gas Industry, With an Example Application in the DJ Basin. Essential Components of an Integrated Data Mining Tool for the Oil & Gas Industry, With an Example Application in the DJ Basin. Petroleum & Natural Gas Engineering West Virginia University SPE Annual Technical

More information

Graduate Co-op Students Information Manual. Department of Computer Science. Faculty of Science. University of Regina

Graduate Co-op Students Information Manual. Department of Computer Science. Faculty of Science. University of Regina Graduate Co-op Students Information Manual Department of Computer Science Faculty of Science University of Regina 2014 1 Table of Contents 1. Department Description..3 2. Program Requirements and Procedures

More information

ANALYTICS IN BIG DATA ERA

ANALYTICS IN BIG DATA ERA ANALYTICS IN BIG DATA ERA ANALYTICS TECHNOLOGY AND ARCHITECTURE TO MANAGE VELOCITY AND VARIETY, DISCOVER RELATIONSHIPS AND CLASSIFY HUGE AMOUNT OF DATA MAURIZIO SALUSTI SAS Copyr i g ht 2012, SAS Ins titut

More information

Fact Sheet FOR PHARMA & LIFE SCIENCES

Fact Sheet FOR PHARMA & LIFE SCIENCES Fact Sheet PATHWAY STUDIO WEB SECURITY OVERVIEW Pathway Studio Web is a comprehensive collection of information with powerful security features to ensure that your research is safe and secure. FOR PHARMA

More information

Access Control And Intrusion Detection For Security In Wireless Sensor Network

Access Control And Intrusion Detection For Security In Wireless Sensor Network Access Control And Intrusion Detection For Security In Wireless Sensor Network Sushma J. Gaurkar, Piyush K.Ingole Abstract: In wireless sensor networks (WSN), security access is one of the key component.

More information

Wireless Intrusion Detection Systems (WIDS)

Wireless Intrusion Detection Systems (WIDS) Systems (WIDS) Dragan Pleskonjic CONWEX Dragan_Pleskonjic@conwex.net dragan@empowerproduction.com Motivation & idea Wireless networks are forecasted to expand rapidly (Wi-Fi IEEE 802.11a/b/g ) WLANs offer

More information

International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163 Volume 1 Issue 11 (November 2014)

International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163 Volume 1 Issue 11 (November 2014) Denial-of-Service Attack Detection Mangesh D. Salunke * Prof. Ruhi Kabra G.H.Raisoni CEM, SPPU, Ahmednagar HOD, G.H.Raisoni CEM, SPPU,Ahmednagar Abstract: A DoS (Denial of Service) attack as name indicates

More information

Recommended 802.11 Wireless Local Area Network Architecture

Recommended 802.11 Wireless Local Area Network Architecture NATIONAL SECURITY AGENCY Ft. George G. Meade, MD I332-008R-2005 Dated: 23 September 2005 Network Hardware Analysis and Evaluation Division Systems and Network Attack Center Recommended 802.11 Wireless

More information

Masters in Human Computer Interaction

Masters in Human Computer Interaction Masters in Human Computer Interaction Programme Requirements Taught Element, and PG Diploma in Human Computer Interaction: 120 credits: IS5101 CS5001 CS5040 CS5041 CS5042 or CS5044 up to 30 credits from

More information

Testing Of Network Intrusion Detection System

Testing Of Network Intrusion Detection System Testing Of Network Intrusion Detection System B.S.Chaitanya Vamsee Pavan KL University,Vijayawada Andhara Pradesh,India bscvpavan369@gmail.com M.Nalini Sri KL University,Vijayawada Andhara Pradesh,India

More information