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Intro; Preface; Acknowledgments; Contents; Contributors; About the Editors; An Android-Based Covert Channel Framework on Wearables Using Status Bar Notifications; 1 Introduction; 2 Background; 2.1 Traditional Threat Model; 2.2 Types of Covert Channels; 2.3 Motivations for Using Covert Channels; 3 Use Cases of Covert Channels; 4 A Novel Covert Channel over Android-Based Notifications; 4.1 Android OS; 4.1.1 Android Notifications and Android Wear; 4.2 Threat Model; 4.3 Covert Channel Framework; 4.3.1 Previous Work; 4.3.2 Timing-Based Framework; 4.3.3 Storage-Based Framework
5 Success of the Covert Channel5.1 Throughput Analysis; 5.1.1 Timing-Based Throughput; 5.1.2 Storage-Based Throughput; 5.2 Covert Analysis; 5.2.1 Pattern Recognition; 5.2.2 CPU Usage; 5.3 Analysis Summary; 6 Discussion and Prevention; 6.1 Prevention; 6.2 Comparison; 7 Trends and Future Work; 7.1 Imminent Threats; 7.2 Future Defenses; 8 Conclusion; References; Insider Threat Detection: Machine Learning Way; 1 Introduction; 1.1 Attack, Launch and Impact; 1.2 Motivations for Attacks; 1.3 Dimensions of Understand Insider Risk; 1.4 Contribution of the Chapter; 1.5 Chapter Organization
2 The Defence Against Insider Threat2.1 Policies and Procedures for Negative Work-Related Events; 2.2 Multimodal Approach for Insider Detection; 3 Approaches in Insider Detection; 3.1 Systemic View for Insider Threat Detection; 3.2 Insider Threat Detection as an Anomaly Detection; 3.2.1 Log Analysis; 3.3 Early Example; 3.4 Anomaly Detection Using Supervised Learning; 3.4.1 Anomaly Detection Using Deep Neural Networks; 3.5 Unsupervised Approach for Anomaly Detection; 3.6 Anomaly Detection Using Game Theoretic Approaches; 3.6.1 Behavioural Relations and Game Theory
3.6.2 Zero Sum Stochastic Game3.6.3 Utility Functions and Equilibrium; 3.7 Anomaly Detection Using Behaviour, Psychology, Criminology and User Profiling; 3.7.1 Anomaly Detection Using Behavioural Analysis; 3.7.2 Deterrence and Social Bond Theory; 3.7.3 Social and Crime Prevention Theories; 3.7.4 Job and Role-Based User Profiling; 4 Case Studies on Insider Threat Defence Mechanism Based on Machine Learning; 4.1 The Dataset; 4.2 Environment; 4.3 Regression and Distance Measurement on Login Activities; 4.3.1 Result Analysis with Cook's Distance; 4.3.2 Result Analysis with Mahalanobis Distance
4.4 Neural Network on Login Activities4.5 SVM on Login Activities; 5 Discussion and Future Research Directions; 6 Conclusion; References; Distributed Denial of Service Attacks and Defense Mechanisms: Current Landscape and Future Directions; 1 Introduction; 2 DDoS Attack Taxonomy and Launch Methods; 2.1 DDoS Attack Taxonomy; 2.2 DDoS Attack Launch Methods and Mechanisms; 3 Reasons for Success; 4 DDoS Attack Defense Methods; 4.1 Prevention Methods; 4.2 Detection Methods; 4.3 Traceback Methods; 4.4 Characterization and Mitigation Methods; 5 Impact, Sophistication and Future Trends; References
5 Success of the Covert Channel5.1 Throughput Analysis; 5.1.1 Timing-Based Throughput; 5.1.2 Storage-Based Throughput; 5.2 Covert Analysis; 5.2.1 Pattern Recognition; 5.2.2 CPU Usage; 5.3 Analysis Summary; 6 Discussion and Prevention; 6.1 Prevention; 6.2 Comparison; 7 Trends and Future Work; 7.1 Imminent Threats; 7.2 Future Defenses; 8 Conclusion; References; Insider Threat Detection: Machine Learning Way; 1 Introduction; 1.1 Attack, Launch and Impact; 1.2 Motivations for Attacks; 1.3 Dimensions of Understand Insider Risk; 1.4 Contribution of the Chapter; 1.5 Chapter Organization
2 The Defence Against Insider Threat2.1 Policies and Procedures for Negative Work-Related Events; 2.2 Multimodal Approach for Insider Detection; 3 Approaches in Insider Detection; 3.1 Systemic View for Insider Threat Detection; 3.2 Insider Threat Detection as an Anomaly Detection; 3.2.1 Log Analysis; 3.3 Early Example; 3.4 Anomaly Detection Using Supervised Learning; 3.4.1 Anomaly Detection Using Deep Neural Networks; 3.5 Unsupervised Approach for Anomaly Detection; 3.6 Anomaly Detection Using Game Theoretic Approaches; 3.6.1 Behavioural Relations and Game Theory
3.6.2 Zero Sum Stochastic Game3.6.3 Utility Functions and Equilibrium; 3.7 Anomaly Detection Using Behaviour, Psychology, Criminology and User Profiling; 3.7.1 Anomaly Detection Using Behavioural Analysis; 3.7.2 Deterrence and Social Bond Theory; 3.7.3 Social and Crime Prevention Theories; 3.7.4 Job and Role-Based User Profiling; 4 Case Studies on Insider Threat Defence Mechanism Based on Machine Learning; 4.1 The Dataset; 4.2 Environment; 4.3 Regression and Distance Measurement on Login Activities; 4.3.1 Result Analysis with Cook's Distance; 4.3.2 Result Analysis with Mahalanobis Distance
4.4 Neural Network on Login Activities4.5 SVM on Login Activities; 5 Discussion and Future Research Directions; 6 Conclusion; References; Distributed Denial of Service Attacks and Defense Mechanisms: Current Landscape and Future Directions; 1 Introduction; 2 DDoS Attack Taxonomy and Launch Methods; 2.1 DDoS Attack Taxonomy; 2.2 DDoS Attack Launch Methods and Mechanisms; 3 Reasons for Success; 4 DDoS Attack Defense Methods; 4.1 Prevention Methods; 4.2 Detection Methods; 4.3 Traceback Methods; 4.4 Characterization and Mitigation Methods; 5 Impact, Sophistication and Future Trends; References