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Data Mining and Machine Learning in Cybersecurity - Sumeet Dua

Data Mining and Machine Learning in Cybersecurity

Hardcover Published: 25th April 2011
ISBN: 9781439839423
Number Of Pages: 256

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With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and journals focus on the fragmented research topics in this area, there has been no single interdisciplinary resource on past and current works and possible paths for future research in this area. This book fills this need.

From basic concepts in machine learning and data mining to advanced problems in the machine learning domain, Data Mining and Machine Learning in Cybersecurity provides a unified reference for specific machine learning solutions to cybersecurity problems. It supplies a foundation in cybersecurity fundamentals and surveys contemporary challenges-detailing cutting-edge machine learning and data mining techniques. It also:

  • Unveils cutting-edge techniques for detecting new attacks
  • Contains in-depth discussions of machine learning solutions to detection problems
  • Categorizes methods for detecting, scanning, and profiling intrusions and anomalies
  • Surveys contemporary cybersecurity problems and unveils state-of-the-art machine learning and data mining solutions
  • Details privacy-preserving data mining methods

This interdisciplinary resource includes technique review tables that allow for speedy access to common cybersecurity problems and associated data mining methods. Numerous illustrative figures help readers visualize the workflow of complex techniques and more than forty case studies provide a clear understanding of the design and application of data mining and machine learning techniques in cybersecurity.

List of Figuresp. xi
List of Tablesp. xv
Prefacep. xvii
Authorsp. xxi
Introductionp. 1
Cybersecurityp. 2
Data Miningp. 5
Machine Learningp. 7
Review of Cybersecurity Solutionsp. 8
Proactive Security Solutionsp. 8
Reactive Security Solutionsp. 9
Misuse/Signature Detectionp. 10
Anomaly Detectionp. 10
Hybrid Detectionp. 13
Scan Detectionp. 13
Profiling Modulesp. 13
Summaryp. 14
Further Readingp. 15
Referencesp. 16
Classical Machine-Learning Paradigms for Data Miningp. 23
Machine Learningp. 24
Fundamentals of Supervised Machine-Learning Methodsp. 24
Association Rule Classificationp. 24
Artificial Neural Networkp. 25
Support Vector Machinesp. 27
Decision Treesp. 29
Bayesian Networkp. 30
Hidden Markov Modelp. 31
Kalman Filterp. 34
Bootstrap, Bagging, and AdaBoostp. 34
Random Forestp. 37
Popular Unsupervised Machine-Learning Methodsp. 38
k-Means Clusteringp. 38
Expectation Maximump. 38
k-Nearest Neighborp. 40
SOM ANNp. 41
Principal Components Analysisp. 41
Subspace Clusteringp. 43
Improvements on Machine-Learning Methodsp. 44
New Machine-Learning Algorithmsp. 44
Resamplingp. 46
Feature Selection Methodsp. 46
Evaluation Methodsp. 47
Cross Validationp. 49
Challengesp. 50
Challenges in Data Miningp. 50
Modeling Large-Scale Networksp. 50
Discovery of Threatsp. 50
Network Dynamics and Cyber Attacksp. 51
Privacy Preservation in Data Miningp. 51
Challenges in Machine Learning (Supervised Learning and Unsupervised Learning)p. 51
Online Learning Methods for Dynamic Modeling of Network Datap. 52
Modeling Data with Skewed Class Distributions to Handle Rare Event Detectionp. 52
Feature Extraction for Data with Evolving Characteristicsp. 53
Research Directionsp. 53
Understanding the Fundamental Problems of Machine-Learning Methods in Cybersecurityp. 54
Incremental Learning in Cyberinfrastructuresp. 54
Feature Selection/Extraction for Data with Evolving Characteristicsp. 54
Privacy-Preserving Data Miningp. 55
Summaryp. 55
Referencesp. 55
Supervised Learning for Misuse/Signature Detectionp. 57
Misuse/Signature Detectionp. 58
Machine Learning in Misuse/Signature Detectionp. 60
Machine-Learning Applications in Misuse Detectionp. 61
Rule-Based Signature Analysisp. 61
Classification Using Association Rulesp. 62
Fuzzy-Rule-Basedp. 65
Artificial Neural Networkp. 68
Support Vector Machinep. 69
Genetic Programmingp. 70
Decision Tree and CARTp. 73
Decision-Tree Techniquesp. 74
Application of a Decision Tree in Misuse Detectionp. 75
CARTp. 77
Bayesian Networkp. 79
Bayesian Network Classifierp. 79
Naïve Bayesp. 82
Summaryp. 82
Referencesp. 82
Machine Learning for Anomaly Detectionp. 85
Introductionp. 85
Anomaly Detectionp. 86
Machine Learning in Anomaly Detection Systemsp. 87
Machine-Learning Applications in Anomaly Detectionp. 88
Rule-Based Anomaly Detection (Table 1.3, C.6)p. 89
Fuzzy Rule-Based (Table 1.3, C.6)p. 90
ANN (Table 1.3, C.9)p. 93
Support Vector Machines (Table 1.3, C.12)p. 94
Nearest Neighbor-Based Learning (Table 1.3, C.ll)p. 95
Hidden Markov Modelp. 98
Kalman Filterp. 99
Unsupervised Anomaly Detectionp. 100
Clustering-Based Anomaly Detectionp. 101
Random Forestsp. 103
Principal Component Analysis/Subspacep. 104
One-Class Supervised Vector Machinep. 106
Information Theoretic (Table 1.3, C.5)p. 110
Other Machine-Learning Methods Applied in Anomaly Detection (Table 1.3, C.2)p. 110
Summaryp. 111
Referencesp. 112
Machine Learning for Hybrid Detectionp. 115
Hybrid Detectionp. 116
Machine Learning in Hybrid Intrusion Detection Systemsp. 118
Machine-Learning Applications in Hybrid Intrusion Detectionp. 119
Anomaly-Misuse Sequence Detection Systemp. 119
Association Rules in Audit Data Analysis and Mining (Table 1.4, D.4)p. 120
Misuse-Anomaly Sequence Detection Systemp. 122
Parallel Detection Systemp. 128
Complex Mixture Detection Systemp. 132
Other Hybrid Intrusion Systemsp. 134
Summaryp. 135
Referencesp. 136
Machine Learning for Scan Detectionp. 139
Scan and Scan Detectionp. 140
Machine Learning in Scan Detectionp. 142
Machine-Learning Applications in Scan Detectionp. 143
Other Scan Techniques with Machine-Learning Methodsp. 156
Summaryp. 156
Referencesp. 157
Machine Learning for Profiling Network Trafficp. 159
Introductionp. 159
Network Traffic Profiling and Related Network Traffic Knowledgep. 160
Machine Learning and Network Traffic Profilingp. 161
Data-Mining and Machine-Learning Applications in Network Profilingp. 162
Other Profiling Methods and Applicationsp. 173
Summaryp. 174
Referencesp. 175
Privacy-Preserving Data Miningp. 177
Privacy Preservation Techniques in PPDMp. 180
Notationsp. 180
Privacy Preservation in Data Miningp. 180
Workflow of PPDMp. 184
Introduction of the PPDM Workflowp. 184
PPDM Algorithmsp. 185
Performance Evaluation of PPDM Algorithmsp. 185
Data-Mining and Machine-Learning Applications in PPDMp. 189
Privacy Preservation Association Rules (Table 1.1, A.4)p. 189
Privacy Preservation Decision Tree (Table 1.1, A.6)p. 193
Privacy Preservation Bayesian Network (Table 1.1, A.2)p. 194
Privacy Preservation KNN (Table 1.1, A.7)p. 197
Privacy Preservation k-Means Clustering (Table 1.1, A.3)p. 199
Other PPDM Methodsp. 201
Summaryp. 202
Referencesp. 204
Emerging Challenges in Cybersecurityp. 207
Emerging Cyber Threatsp. 208
Threats from Malwarep. 208
Threats from Botnetsp. 209
Threats from Cyber Warfarep. 211
Threats from Mobile Communicationp. 211
Cyber Crimesp. 212
Network Monitoring, Profiling, and Privacy Preservationp. 213
Privacy Preservation of Original Datap. 213
Privacy Preservation in the Network Traffic Monitoring and Profiling Algorithmsp. 214
Privacy Preservation of Monitoring and Profiling Datap. 215
Regulation, Laws, and Privacy Preservationp. 215
Privacy Preservation, Network Monitoring, and Profiling Example: PRISMp. 216
Emerging Challenges in Intrusion Detectionp. 218
Unifying the Current Anomaly Detection Systemsp. 219
Network Traffic Anomaly Detectionp. 219
Imbalanced Learning Problem and Advanced Evaluation Metrics for IDSp. 220
Reliable Evaluation Data Sets or Data Generation Toolsp. 221
Privacy Issues in Network Anomaly Detectionp. 222
Summaryp. 222
Referencesp. 223
Indexp. 225
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9781439839423
ISBN-10: 1439839425
Audience: Tertiary; University or College
Format: Hardcover
Language: English
Number Of Pages: 256
Published: 25th April 2011
Publisher: Taylor & Francis Ltd
Country of Publication: GB
Dimensions (cm): 22.86 x 15.24  x 1.91
Weight (kg): 0.52
Edition Number: 1