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Preface; Acknowledgements; Contents; Measuring the Overall Network Security by Combining CVSS Scores Based on Attack Graphs and Bayesian Networks; 1 Introduction; 2 Propagating Attack Probabilities Along Attack Paths; 2.1 Motivating Example; 2.2 Defining the Metric; 2.3 Handling Cycles in Attack Graphs; 3 Bayesian Network-Based Attack Graph Model; 3.1 Representing Attack Graphs Using BNs; 3.2 Comparing to the Previous Approach; 4 Dynamic Bayesian Network-Based Model; 4.1 The General Model; 4.2 Case 1: Inferring Exploit Node Values; 4.3 Case 2: Inferring TGS Node Values; 5 Conclusion
4.2 Simulation Results5 Conclusion; References; Security Risk Analysis of Enterprise Networks Using Probabilistic Attack Graphs; 1 Introduction; 2 Attack Graphs; 2.1 Tools for Generating Attack Graphs; 3 Past Work in Security Risk Analysis; 4 Common Vulnerability Scoring System (CVSS); 4.1 An Example; 5 Security Risk Analysis of Enterprise Networks Using Attack Graphs; 5.1 Example 1; 5.1.1 Overview; 5.2 Example 2; 5.3 Example 3; 5.4 Using Metrics to Prioritize Risk Mitigation; 6 Challenges; 7 Conclusions; References
K-Zero Day Safety: Evaluating the Resilience of Networks Against Unknown Attacks1 Introduction; 2 Motivating Example; 3 Modeling k-Zero Day Safety; 4 Applying k-Zero Day Safety; 4.1 Redefining Network Hardening; 4.2 Instantiating the Model; 5 Case Study; 5.1 Diversity; 5.2 Known Vulnerability and Unnecessary Service; 5.3 Backup of Asset; 5.4 Firewall; 5.5 Stuxnet and SCADA Security; 6 Conclusion; References; Using Bayesian Networks to Fuse Intrusion Evidences and Detect Zero-Day Attack Paths; 1 Motivation; 2 Rationales and Models; 2.1 Rationales of Using Bayesian Networks
2.2 Problems of Constructing BN Based on SODG2.3 Object Instance Graph; 3 Instance-Graph-Based Bayesian Networks; 3.1 The Infection Propagation Models; 3.2 Evidence Incorporation; 4 System Overview; 5 Implementation; 6 Evaluation; 6.1 Attack Scenario; 6.2 Experiment Results; 7 Conclusion; References; Evaluating the Network Diversity of Networks Against Zero-DayAttacks; 1 Introduction; 2 Use Cases; 2.1 Use Case 1: Stuxnet and SCADA Security; 2.2 Use Case 2: Worm Propagation; 2.3 Use Case 3: Targeted Attack; 2.4 Use Case 4: MTD; 3 Biodiversity-Inspired Network Diversity Metric
4.2 Simulation Results5 Conclusion; References; Security Risk Analysis of Enterprise Networks Using Probabilistic Attack Graphs; 1 Introduction; 2 Attack Graphs; 2.1 Tools for Generating Attack Graphs; 3 Past Work in Security Risk Analysis; 4 Common Vulnerability Scoring System (CVSS); 4.1 An Example; 5 Security Risk Analysis of Enterprise Networks Using Attack Graphs; 5.1 Example 1; 5.1.1 Overview; 5.2 Example 2; 5.3 Example 3; 5.4 Using Metrics to Prioritize Risk Mitigation; 6 Challenges; 7 Conclusions; References
K-Zero Day Safety: Evaluating the Resilience of Networks Against Unknown Attacks1 Introduction; 2 Motivating Example; 3 Modeling k-Zero Day Safety; 4 Applying k-Zero Day Safety; 4.1 Redefining Network Hardening; 4.2 Instantiating the Model; 5 Case Study; 5.1 Diversity; 5.2 Known Vulnerability and Unnecessary Service; 5.3 Backup of Asset; 5.4 Firewall; 5.5 Stuxnet and SCADA Security; 6 Conclusion; References; Using Bayesian Networks to Fuse Intrusion Evidences and Detect Zero-Day Attack Paths; 1 Motivation; 2 Rationales and Models; 2.1 Rationales of Using Bayesian Networks
2.2 Problems of Constructing BN Based on SODG2.3 Object Instance Graph; 3 Instance-Graph-Based Bayesian Networks; 3.1 The Infection Propagation Models; 3.2 Evidence Incorporation; 4 System Overview; 5 Implementation; 6 Evaluation; 6.1 Attack Scenario; 6.2 Experiment Results; 7 Conclusion; References; Evaluating the Network Diversity of Networks Against Zero-DayAttacks; 1 Introduction; 2 Use Cases; 2.1 Use Case 1: Stuxnet and SCADA Security; 2.2 Use Case 2: Worm Propagation; 2.3 Use Case 3: Targeted Attack; 2.4 Use Case 4: MTD; 3 Biodiversity-Inspired Network Diversity Metric