Adversary-Aware Learning Techniques and Trends in Cybersecurity

Part I: Game-Playing AI and Game Theory-based Techniques for Cyber Defenses.- 1. Rethinking Intelligent Behavior as Competitive Games for Handling Adversarial Challenges to Machine Learning.- 2. Security of Distributed Machine Learning:A Game-Theoretic Approach to Design Secure DSVM.- 3. Be Careful When Learning Against Adversaries: Imitative Attacker Deception in Stackelberg Security Games.- Part II: Data Modalities and Distributed Architectures for Countering Adversarial Cyber Attacks.- 4. Adversarial Machine Learning in Text: A Case Study of Phishing Email Detection with RCNN model.- 5. Overview of GANs for Image Synthesis and Detection Methods.- 6. Robust Machine Learning using Diversity and Blockchain.- Part III: Human Machine Interactions and Roles in Automated Cyber Defenses.- 7. Automating the Investigation of Sophisticated Cyber Threats with Cognitive Agents.- 8. Integrating Human Reasoning and Machine Learning to Classify Cyber Attacks.- 9. Homology as an Adversarial Attack Indicator.- Cyber-(in)security, revisited: Proactive Cyber-defenses, Interdependence and Autonomous Human Machine Teams (A-HMTs).