Game theory and machine learning for cyber security Introduction to game theory Fei Fang Shutian Liu Anjon Basak Quanyan Zhu Christopher Kiekintveld Charles A. Kamhoua Scalable algorithms for identifying stealthy attackers in a game theoretic framework using deception Anjon Basak Charles Kamhoua Sridhar Venkatesan Marcus Gutierrez Ahmed H. Anwar Christopher Kiekintveld Honeypot allocation game over attack graphs for cyber deception Ahmed H. Anwar Charles Kamhoua Nandi Leslie Christopher Kiekintveld. Cyber security is a serious concern to our economic prosperity and national security. Despite an increased investment in cyber defense cyber attackers are becoming more creative and sophisticated. This exposes the need for a more rigorous approach to cyber security including methods from artificial intelligence including computational game theory and machine learning. Recent advances in adversarial machine learning are promising to make artificial intelligence (AI) algorithms more robust to deception and intelligent manipulation. However they are still vulnerable to adversarial inputs data poisoning model stealing and evasion attacks. The above challenges and the high risk and consequence of cyber attacks drive the need to accelerate basic research on cyber security