Adversary-Aware Learning Techniques and Trends in Cybersecurity

Part I: Game-Playing AI and Game Theory-based Techniques for Cyber Defenses

Rethinking Intelligent Behavior as Competitive Games for Handling Adversarial Challenges to Machine Learning

Joseph B Collins and Prithviraj Dasgupta

Security of Distributed Machine Learning:A Game-Theoretic Approach to Design Secure DSVM

Rui Zhang and Quanyan Zhu

Be Careful When Learning Against Adversaries: Imitative Attacker Deception in Stackelberg Security Games

Haifeng Xu and Thanh H. Nguyen

Part II: Data Modalities and Distributed Architectures for Countering Adversarial Cyber Attacks

Adversarial Machine Learning in Text: A Case Study of Phishing Email Detection with RCNN model

Daniel Lee and Rakesh M. Verma

Overview of GANs for Image Synthesis and Detection Methods

Eric Tjon, Melody Moh and Teng-Sheng Moh

Robust Machine Learning using Diversity and Blockchain

Raj Mani Shukla, Shahriar Badsha, Deepak Tosh, and Shamik Sengupta

Part III: Human Machine Interactions and Roles in Automated Cyber Defenses

Automating the Investigation of Sophisticated Cyber Threats with Cognitive Agents

Steven Meckl, Gheorghe Tecuci, Dorin Marcu and Mihai Boicu

Integrating Human Reasoning and Machine Learning to Classify Cyber Attacks

Ying Zhao and Lauren Jones

Homology as an Adversarial Attack Indicator

Ira S. Moskowitz, Nolan Bay, Brian Jalaian and Arnold Tunick

Cyber-(in)security, revisited: Proactive Cyber-defenses, Interdependence and Autonomous Human Machine Teams (A-HMTs)

William Lawless, Ranjeev Mittu, Ira Moskowitz, Donald Sofge and Stephen Russell