Preface -- Contents -- Chapter 1 Introduction -- 1.1. Background -- 1.2. Existing Problems -- 1.2.1. Alarm management -- 1.2.2. Performance maintenance -- Chapter 2 Attacks and Countermeasures in Computer Security -- 2.1. General Security Objectives -- 2.1.1. Accountability -- 2.1.2. Assurance -- 2.1.3. Authentication -- 2.1.4. Authorization -- 2.1.5. Availability -- 2.1.6. Confidentiality -- 2.1.7. Integrity -- 2.1.8. Non-repudiation -- 2.2. Types of Attacks -- 2.2.1. Attacks against availability -- 2.2.2. Attacks against confidentiality -- 2.2.3. Attacks against integrity -- 2.2.4. Attacks against miscellaneous security objectives -- 2.3. Countermeasures of Attacks -- 2.3.1. Authentication -- 2.3.2. Access control -- 2.3.3. Audit and intrusion detection -- 2.3.4. Extrusion detection -- 2.3.5. Cryptography -- 2.3.6. Firewall -- 2.3.7. Anti-virus software -- Chapter 3 Machine Learning Methods -- 3.1. Background -- 3.2. Concept Learning -- 3.3. Decision Tree -- 3.4. Neural Networks -- 3.5. Bayesian Learning -- 3.6. Genetic Algorithms and Genetic Programming -- 3.7. Instance-Based Learning -- 3.8. Inductive Logic Programming -- 3.9. Analytical Learning -- 3.10. Inductive and Analytical Learning -- 3.11. Reinforcement Learning -- 3.12. Ensemble Learning -- 3.13. Multiple Instance Learning -- 3.14. Unsupervised Learning -- 3.15. Semi-Supervised Learning -- 3.16. Support Vector Machines -- Chapter 4 Intrusion Detection System -- 4.1. Background -- 4.1.1. Security defense in depth -- 4.1.2. A brief history of intrusion detection -- 4.1.3. Classification of intrusion detection system -- 4.1.4. Standardization efforts -- 4.1.5. General model of intrusion detection system -- 4.2. Available Audit Data -- 4.2.1. System features -- 4.2.2. User activities -- 4.2.3. Network activities -- 4.3. Preprocess Methods -- 4.4. Detection Methods.

4.4.1. Statistical analysis -- 4.4.2. Expert system -- 4.4.3. Model-based system -- 4.4.4. State transition-based analysis -- 4.4.5. Neural network-based system -- 4.4.6. Data mining-based system -- 4.5. Architecture for Network Intrusion Detection System -- Part A Intrusion Detection for Wired Network -- Chapter 5 Techniques for Intrusion Detection -- 5.1. Available Alarm Management Solutions -- 5.1.1. Alarm correlation -- 5.1.2. Alarm filter -- 5.1.3. Event classification process -- 5.2. Available Performance Maintenance Solutions -- 5.2.1. Adaptive learning -- 5.2.2. Incremental mining -- Chapter 6 Adaptive Automatically Tuning Intrusion Detection System -- 6.1. Architecture -- 6.2. SOM-Based Labeling Tool -- 6.2.1. Training algorithm -- 6.2.2. Pre-cluster by symbolic features -- 6.2.3. Cluster by SOM -- 6.2.4. Label data in clusters -- 6.3. Hybrid Detection Model -- 6.3.1. Binary SLIPPER rule learning system -- 6.3.2. Binary classifiers -- 6.3.3. Final arbiter -- 6.3.4. Detection model tuning -- 6.3.5. Fuzzy prediction filter -- 6.3.6. Fuzzy tuning controller -- Chapter 7 System Prototype and Performance Evaluation -- 7.1. Implementation of Prototype -- 7.1.1. Fuzzy controller -- 7.1.2. Binary prediction and model tuning thread -- 7.1.3. Final arbiter and prediction filter thread -- 7.1.4. User simulator thread -- 7.1.5. Interface for fuzzy knowledge base -- 7.2. Experimental Data set and Related Systems -- 7.2.1. KDDCup'99 intrusion detection data set -- 7.2.2. Performance evaluation method -- 7.2.3. Related IDSs on KDDCup'99 ID data set -- 7.3. Performance Evaluation -- 7.3.1. SOM-based labeling tool performance -- 7.3.2. Build hybrid detection model -- 7.3.3. The MC-SLIPPER system and test performance -- 7.3.4. The ATIDS system and test performance -- 7.3.5. The ADAT IDS system and test performance.

Part B Intrusion Detection for Wireless Sensor Network -- Chapter 8 Attacks against Wireless Sensor Network -- 8.1. Wireless Sensor Network -- 8.2. Challenges on Intrusion Detection in WSNs -- 8.3. Attacks against WSNs -- Chapter 9 Intrusion Detection System for Wireless Sensor Network -- 9.1. Architecture of IDS for WSN -- 9.2. Audit Data in WSN -- 9.2.1. Local features for LIDC in WSN -- 9.2.2. Packet features for PIDC in WSN -- 9.3. Detection Model and Optimization -- 9.4. Model Tuning -- Chapter 10 Conclusion and Future Research -- Cited Literature -- Index.