Abstract Advanced Persistent Threats (APTs) have recently emerged as a significant security
challenge for a cyber-physical system due to their stealthy, dynamic and adaptive nature …

We analyze a scenario in which software agents implemented as regret-minimizing
algorithms engage in a repeated auction on behalf of their users. We study first-price and …

The usage of automated learning agents is becoming increasingly prevalent in many online
economic applications such as online auctions and automated trading. Motivated by such …

Cyber deception is one of the key approaches used to mislead attackers by hiding or
providing inaccurate system information. There are two main factors limiting the real-world …

A significant robustness gap exists between machine intelligence and human perception
despite recent advances in deep learning. Deep learning is not provably secure. A critical …

To address the challenge of uncertainty regarding the attacker's payoffs, capabilities and
other characteristics, recent work in security games has focused on learning the optimal …

Learning attacker behavior is an important research topic in security games as security
agencies are often uncertain about attackers' decision making. Previous work has focused …

The prevalence of security threats to organizational defense demands models that support
real-world policymaking. Security games are a potent tool in this regard; however, although …

We consider a team of autonomous agents that navigate in an adversarial environment and
aim to achieve a task by allocating their resources over a set of target locations. An …

We study the design of autonomous agents that are capable of deceiving outside observers
about their intentions while carrying out tasks in stochastic, complex environments. By …