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E-raamat: Intrusion Detection: A Machine Learning Approach

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(Asia Univ, Taiwan & Univ Of Illinois At Chicago, Usa), (Univ Of Illinois, Chicago, Usa)
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Intrusion Detection: A Machine Learning ApproachSuurem pilt
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This important book introduces the concept of intrusion detection, discusses various approaches for intrusion detection systems (IDS), and presents the architecture and implementation of IDS. It emphasizes on the prediction and learning algorithms for intrusion detection and highlights techniques for intrusion detection of wired computer networks and wireless sensor networks. The performance comparison of various IDS via simulation will also be included.
Preface vii
1 Introduction 1(6)
1.1 Background
1(2)
1.2 Existing Problems
3(4)
1.2.1 Alarm management
3(1)
1.2.2 Performance maintenance
4(3)
2 Attacks and Countermeasures in Computer Security 7(24)
2.1 General Security Objectives
7(3)
2.1.1 Accountability
7(1)
2.1.2 Assurance
8(1)
2.1.3 Authentication
8(1)
2.1.4 Authorization
8(1)
2.1.5 Availability
8(1)
2.1.6 Confidentiality
9(1)
2.1.7 Integrity
9(1)
2.1.8 Non-repudiation
9(1)
2.2 Types of Attacks
10(4)
2.2.1 Attacks against availability
10(1)
2.2.2 Attacks against confidentiality
11(1)
2.2.3 Attacks against integrity
12(1)
2.2.4 Attacks against miscellaneous security objectives
13(1)
2.3 Countermeasures of Attacks
14(17)
2.3.1 Authentication
15(1)
2.3.2 Access control
16(4)
2.3.3 Audit and intrusion detection
20(2)
2.3.4 Extrusion detection
22(1)
2.3.5 Cryptography
23(3)
2.3.6 Firewall
26(2)
2.3.7 Anti-virus software
28(3)
3 Machine Learning Methods 31(8)
3.1 Background
31(1)
3.2 Concept Learning
31(1)
3.3 Decision Tree
32(1)
3.4 Neural Networks
32(1)
3.5 Bayesian Learning
32(1)
3.6 Genetic Algorithms and Genetic Programming
33(1)
3.7 Instance-Based Learning
33(1)
3.8 Inductive Logic Programming
34(1)
3.9 Analytical Learning
34(1)
3.10 Inductive and Analytical Learning
34(1)
3.11 Reinforcement Learning
35(1)
3.12 Ensemble Learning
35(1)
3.13 Multiple Instance Learning
36(1)
3.14 Unsupervised Learning
36(1)
3.15 Semi-Supervised Learning
36(1)
3.16 Support Vector Machines
37(2)
4 Intrusion Detection System 39(22)
4.1 Background
39(5)
4.1.1 Security defense in depth
39(2)
4.1.2 A brief history of intrusion detection
41(1)
4.1.3 Classification of intrusion detection system
41(2)
4.1.4 Standardization efforts
43(1)
4.1.5 General model of intrusion detection system
43(1)
4.2 Available Audit Data
44(3)
4.2.1 System features
44(1)
4.2.2 User activities
45(1)
4.2.3 Network activities
46(1)
4.3 Preprocess Methods
47(2)
4.4 Detection Methods
49(7)
4.4.1 Statistical analysis
49(2)
4.4.2 Expert system
51(1)
4.4.3 Model-based system
51(1)
4.4.4 State transition-based analysis
52(1)
4.4.5 Neural network-based system
53(1)
4.4.6 Data mining-based system
54(2)
4.5 Architecture for Network Intrusion Detection System
56(5)
Part A: Intrusion Detection for Wired Network
5 Techniques for Intrusion Detection
61(4)
5.1 Available Alarm Management Solutions
61(2)
5.1.1 Alarm correlation
61(1)
5.1.2 Alarm filter
62(1)
5.1.3 Event classification process
63(1)
5.2 Available Performance Maintenance Solutions
63(2)
5.2.1 Adaptive learning
63(1)
5.2.2 Incremental mining
64(1)
6 Adaptive Automatically Tuning Intrusion Detection System
65(36)
6.1 Architecture
65(1)
6.2 SOM-Based Labeling Tool
65(6)
6.2.1 Training algorithm
66(2)
6.2.2 Pre-cluster by symbolic features
68(1)
6.2.3 Cluster by SOM
68(2)
6.2.4 Label data in clusters
70(1)
6.3 Hybrid Detection Model
71(30)
6.3.1 Binary SLIPPER rule learning system
71(3)
6.3.2 Binary classifiers
74(1)
6.3.3 Final arbiter
74(5)
6.3.4 Detection model tuning
79(7)
6.3.5 Fuzzy prediction filter
86(10)
6.3.6 Fuzzy tuning controller
96(5)
7 System Prototype and Performance Evaluation
101(40)
7.1 Implementation of Prototype
101(2)
7.1.1 Fuzzy controller
101(1)
7.1.2 Binary prediction and model tuning thread
101(1)
7.1.3 Final arbiter and prediction filter thread
102(1)
7.1.4 User simulator thread
102(1)
7.1.5 Interface for fuzzy knowledge base
103(1)
7.2 Experimental Data set and Related Systems
103(9)
7.2.1 KDDCup'99 intrusion detection data set
103(2)
7.2.2 Performance evaluation method
105(3)
7.2.3 Related IDSs on KDDCup'99 ID data set
108(4)
7.3 Performance Evaluation
112(29)
7.3.1 SOM-based labeling tool performance
112(2)
7.3.2 Build hybrid detection model
114(2)
7.3.3 The MC-SLIPPER system and test performance
116(9)
7.3.4 The ATIDS system and test performance
125(8)
7.3.5 The ADAT IDS system and test performance
133(8)
Part B: Intrusion Detection for Wireless Sensor Network
8 Attacks against Wireless Sensor Network
141(6)
8.1 Wireless Sensor Network
141(1)
8.2 Challenges on Intrusion Detection in WSNs
142(1)
8.3 Attacks against WSNs
143(4)
9 Intrusion Detection System for Wireless Sensor Network
147(10)
9.1 Architecture of IDS for WSN
147(2)
9.2 Audit Data in WSN
149(4)
9.2.1 Local features for LIDC in WSN
150(2)
9.2.2 Packet features for PIDC in WSN
152(1)
9.3 Detection Model and Optimization
153(2)
9.4 Model Tuning
155(2)
10 Conclusion and Future Research
157(2)
Cited Literature 159(10)
Index 169
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