Moving Target Defense: A Symbiotic Framework for AI & Security

Modern day technology has found its way into every aspect of our lives{be it the server storing our social information, the hand-held smartphones, the home security systems or a remotely monitored pacemaker. Unfortunately, this also increases the opportunity for agents with malicious intent to violate the privacy, availability or integrity of these applications. In fact, with the advancement of Artificial Intelligence (AI) and faster hardware, the process of finding and exploiting vulnerabilities is no longer as time-consuming as before. Moving Target Defense (MTD) is emerging as an effective technique in addressing these security concerns. This technique, as used by the cyber security community, however, does not incorporate the dynamics of a multi-agent system between an attacker and defender, resulting in sub-optimal behavior. My study of such systems in a multi-agent context helps to enhance the security of MTD systems and proposes a list of challenges for the AI community. Furthermore, borrowing the example of MTD systems from the cyber security community, we can address some security concerns of the present day AI algorithms. In this abstract, I describe my research work that uses AI for enhancing security of a multi-agent MTD system and highlight research avenues in using MTD for enhancing security of present AI algorithms.