Cyber warfare : techniques, tactics and tools for security practitioners

434 Pages·2011·4.922 MB·English

by  Jason Andress, Steve Winterfeld, Russ Rogers

Computer Science / Computer Engineering / Computing T M
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Data Mining Tools for Malware Detection r s
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Mehedy Masud, Latifur Khan, and Bhavani Thuraisingham in •

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Although the use of data mining for security and malware detection is quickly a h
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on the rise, most books on the subject provide high-level theoretical discussions n
to the near exclusion of the practical aspects. Breaking the mold, Data Mining
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Tools for Malware Detection provides a step-by-step breakdown of how to
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develop data mining tools for malware detection. Integrating theory with practical
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techniques and experimental results, it focuses on malware detection applications a

for email worms, malicious code, remote exploits, and botnets. M
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The authors describe the systems they have designed and developed: email worm n
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detection using data mining, a scalable multi-level feature extraction technique n
to detect malicious executables, detecting remote exploits using data mining, and g

flow-based identification of botnet traffic by mining multiple log files. For each of
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these tools, they detail the system architecture, algorithms, performance results, o
and limitations. o
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• Discusses data mining for emerging applications, including adaptable

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malware detection, insider threat detection, firewall policy analysis,
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and real-time data mining r

• Includes four appendices that provide a firm foundation in data M
management, secure systems, and the semantic web a
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• Describes the authors’ tools for stream data mining w
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From algorithms to experimental results, this is one of the few books that will r
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be equally valuable to those in industry, government, and academia. It will help

technologists decide which tools to select for specific applications, managers will D
learn how to determine whether or not to proceed with a data mining project, and e
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developers will find innovative alternative designs for a range of applications.
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K12530 t
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ISBN: 978-1-4398-5454-9
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www.crcpress.com
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K12530 cvr mech.indd 1 10/27/11 10:09 AM
Data Mining
Tools for Malware
Detection
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Data Mining
Tools for Malware
Detection
Mehedy Masud, Latifur Khan,
and Bhavani Thuraisingham
CRC Press
Taylor & Francis Group
6000 Broken Sound Parkway NW, Suite 300
Boca Raton, FL 33487-2742
© 2012 by Taylor & Francis Group, LLC
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Version Date: 20111020
International Standard Book Number-13: 978-1-4398-5455-6 (eBook - PDF)
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Dedication
We dedicate this book to our respective families for their support that
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Contents
Preface xix
Introductory Remarks xix
Background on Data Mining xx
Data Mining for Cyber Security xxii
Organization of This Book xxii
Concluding Remarks xxiii
acknowledgments xxv
the authors xxvii
coPyright Permissions xxix
chaPter 1 introduction 1
1.1 Trends 1
1.2 Data Mining and Security Technologies 2
1.3 Data Mining for Email Worm Detection 3
1.4 Data Mining for Malicious Code Detection 4
1.5 Data Mining for Detecting Remote Exploits 5
1.6 Data Mining for Botnet Detection 5
1.7 Stream Data Mining 5
1.8 Emerging Data Mining Tools for Cyber Security
Applications 6
1.9 Organization of This Book 6
1.10 Next Steps 7
Part i data mining and security
Introduction to Part I: Data Mining and Security 11
vii
viii Contents
chaPter 2 data mining techniques 13
2.1 Introduction 13
2.2 Overview of Data Mining Tasks and Techniques 14
2.3 Artificial Neural Network 14
2.4 Support Vector Machines 19
2.5 Markov Model 22
2.6 Association Rule Mining (ARM) 25
2.7 Multi-Class Problem 29
2.7.1 One-vs-One 29
2.7.2 One-vs-All 30
2.8 Image Mining 31
2.8.1 Feature Selection 32
2.8.2 Automatic Image Annotation 33
2.8.3 Image Classification 33
2.9 Summary 34
References 34
chaPter 3 malware 37
3.1 Introduction 37
3.2 Viruses 38
3.3 Worms 39
3.4 Trojan Horses 40
3.5 Time and Logic Bombs 40
3.6 Botnet 41
3.7 Spyware 42
3.8 Summary 42
References 43
chaPter 4 data mining for security aPPlications 45
4.1 Introduction 45
4.2 Data Mining for Cyber Security 46
4.2.1 Overview 46
4.2.2 Cyber-Terrorism, Insider Threats, and
External Attacks 47
4.2.3 Malicious Intrusions 48
4.2.4 Credit Card Fraud and Identity Theft 49
4.2.5 Attacks on Critical Infrastructures 50
4.2.6 Data Mining for Cyber Security 50
4.3 Current Research and Development 52
4.4 Summary 54
References 54
chaPter 5 design and imPlementation of data mining
tools 57
5.1 Introduction 57
5.2 Intrusion Detection 59
5.3 Web Page Surfing Prediction 62
Contents ix
5.4 Image Classification 65
5.5 Summary 66
References 66
conclusion to Part i 69
Part ii data mining for email worm detection
Introduction to Part II 71
chaPter 6 email worm detection 73
6.1 Introduction 73
6.2 Architecture 74
6.3 Related Work 75
6.4 Overview of Our Approach 76
6.5 Summary 77
References 78
chaPter 7 design of the data mining tool 81
7.1 Introduction 81
7.2 Architecture 82
7.3 Feature Description 83
7.3.1 Per-Email Features 83
7.3.2 Per-Window Features 84
7.4 Feature Reduction Techniques 84
7.4.1 Dimension Reduction 84
7.4.2 Two-Phase Feature Selection (TPS) 85
7.4.2.1 Phase I 85
7.4.2.2 Phase II 87
7.5 Classification Techniques 89
7.6 Summary 91
References 92
chaPter 8 evaluation and results 95
8.1 Introduction 95
8.2 Dataset 96
8.3 Experimental Setup 98
8.4 Results 99
8.4.1 Results from Unreduced Data 99
8.4.2 Results from PCA-Reduced Data 99
8.4.3 Results from Two-Phase Selection 102
8.5 Summary 106
References 106
conclusion to Part ii 107
Part iii data mining for detecting
malicious executables
Introduction to Part III 109