| Network Attacks | p. 1 |
| Attack Taxonomies | p. 2 |
| Probes | p. 4 |
| EPSweep and PortSweep | p. 5 |
| NMap | p. 5 |
| MScan | p. 5 |
| SAINT | p. 5 |
| Satan | p. 6 |
| Privilege Escalation Attacks | p. 6 |
| Buffer Overflow Attacks | p. 7 |
| Misconfiguration Attacks | p. 7 |
| Race-condition Attacks | p. 8 |
| Man-in-the-Middle Attacks | p. 9 |
| Social Engineering Attacks | p. 10 |
| Denial of Service (DoS) and Distributed Denial of Service (DDoS) Attacks | p. 11 |
| Detection Approaches for DoS and DDoS Attacks | p. 11 |
| Prevention and Response for DoS and DDoS Attacks | p. 13 |
| Examples of DoS and DDoS Attacks | p. 14 |
| Worms Attacks | p. 16 |
| Modeling and Analysis of Worm Behaviors | p. 16 |
| Detection and Monitoring of Worm Attacks | p. 17 |
| Worms Containment | p. 18 |
| Examples of Well Known Worm Attacks | p. 19 |
| Routing Attacks | p. 19 |
| OSPF Attacks | p. 20 |
| BGP Attacks | p. 21 |
| References | p. 22 |
| Detection Approaches | p. 27 |
| Misuse Detection | p. 27 |
| Pattern Matching | p. 28 |
| Rule-based Techniques | p. 29 |
| State-based Techniques | p. 31 |
| Techniques based on Data Mining | p. 34 |
| Anomaly Detection | p. 34 |
| Advanced Statistical Models | p. 36 |
| Rule based Techniques | p. 37 |
| Biological Models | p. 39 |
| Learning Models | p. 40 |
| Specification-based Detection | p. 45 |
| Hybrid Detection | p. 46 |
| References | p. 49 |
| Data Collection | p. 55 |
| Data Collection for Host-Based IDSs | p. 55 |
| Audit Logs | p. 56 |
| System Call Sequences | p. 58 |
| Data Collection for Network-Based IDSs | p. 61 |
| SNMP | p. 61 |
| Packets | p. 62 |
| Limitations of Network-Based IDSs | p. 66 |
| Data Collection for Application-Based IDSs | p. 67 |
| Data Collection for Application-Integrated IDSs | p. 68 |
| Hybrid Data Collection | p. 69 |
| References | p. 69 |
| Theoretical Foundation of Detection | p. 73 |
| Taxonomy of Anomaly Detection Systems | p. 73 |
| Fuzzy Logic | p. 75 |
| Fuzzy Logic in Anomaly Detection | p. 77 |
| Bayes Theory | p. 77 |
| Naive Bayes Classifier | p. 78 |
| Bayes Theory in Anomaly Detection | p. 78 |
| Artificial Neural Networks | p. 79 |
| Processing Elements | p. 79 |
| Connections | p. 82 |
| Network Architectures | p. 83 |
| Learning Process | p. 84 |
| Artificial Neural Networks in Anomaly Detection | p. 85 |
| Support Vector Machine (SVM) | p. 86 |
| Support Vector Machine in Anomaly Detection | p. 89 |
| Evolutionary Computation | p. 89 |
| Evolutionary Computation in Anomaly Detection | p. 91 |
| Association Rules | p. 92 |
| The Apriori Algorithm | p. 93 |
| Association Rules in Anomaly Detection | p. 93 |
| Clustering | p. 94 |
| Taxonomy of Clustering Algorithms | p. 95 |
| K-Means Clustering | p. 96 |
| Y-Means Clustering | p. 97 |
| Maximum-Likelihood Estimates | p. 98 |
| Unsupervised Learning of Gaussian Data | p. 100 |
| Clustering Based on Density Distribution Functions | p. 101 |
| Clustering in Anomaly Detection | p. 102 |
| Signal Processing Techniques Based Models | p. 104 |
| Comparative Study of Anomaly Detection Techniques | p. 109 |
| References | p. 110 |
| Architecture and Implementation | p. 115 |
| Centralized | p. n5 |
| Distributed | p. 115 |
| Intelligent Agents | p. 116 |
| Mobile Agents | p. 123 |
| Cooperative Intrusion Detection | p. 125 |
| References | p. 126 |
| Alert Management and Correlation | p. 129 |
| Data Fusion | p. 129 |
| Alert Correlation | p. 131 |
| Preprocess | p. 132 |
| Correlation Techniques | p. 139 |
| Postprocess | p. 145 |
| Alert Correlation Architectures | p. 150 |
| Validation of Alert Correlation Systems | p. 152 |
| Cooperative Intrusion Detection | p. 153 |
| Basic Principles of Information Sharing | p. 153 |
| Cooperation Based on Goal-tree Representation of Attack Strategies | p. 154 |
| Cooperative Discovery of Intrusion Chain | p. 154 |
| Abstraction-Based Intrusion Detection | p. 155 |
| Interest-Biased Communication and Cooperation | p. 155 |
| Agent-Based Cooperation | p. 156 |
| Secure Communication Using Public-key Encryption | p. 157 |
| References | p. 157 |
| Evaluation Criteria | p. 161 |
| Accuracy | p. 161 |
| False Positive and Negative | p. 162 |
| Confusion Matrix | p. 163 |
| Precision, Recall, and F-Measure | p. 164 |
| ROC Curves | p. 166 |
| The Base-Rate Fallacy | p. 168 |
| Performance | p. 171 |
| Completeness | p. 172 |
| Timely Response | p. 172 |
| Adaptation and Cost-Sensitivity | p. 175 |
| Intrusion Tolerance and Attack Resistance | p. 177 |
| Redundant and Fault Tolerance Design | p. 177 |
| Obstructing Methods | p. 179 |
| Test, Evaluation and Data Sets | p. 180 |
| References | p. 182 |
| Intrusion Response | p. 185 |
| Response Type | p. 185 |
| Passive Alerting and Manual Response | p. 185 |
| Active Response | p. 186 |
| Response Approach | p. 186 |
| Decision Analysis | p. 186 |
| Control Theory | p. 189 |
| Game theory | p. 189 |
| Fuzzy theory | p. 190 |
| Survivability and Intrusion Tolerance | p. 194 |
| References | p. 197 |
| Examples of Commercial and Open Source IDSs | p. 199 |
| Bro Intrusion Detection System | p. 199 |
| Prelude Intrusion Detection System | p. 199 |
| Snort Intrusion Detection System | p. 200 |
| Ethereal Application - Network Protocol Analyzer | p. 200 |
| Multi Router Traffic Grapher (MRTG) | p. 201 |
| Tamandua Network Intrusion Detection System | p. 202 |
| Other Commercial IDSs | p. 202 |
| Index | p. 209 |
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