Adversary-aware learning techniques and trends in cybersecurity / Prithviraj Dasgupta, Joseph B. Collins, Ranjeev Mittu, editors.

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).