A standard model of a security game assumes a one-off assault during which the attacker cannot update their strategy even if new actionable insights are gained in the process. In this paper, we propose a version of a security game that takes into account ...

Machine-learning-as-a-service (MLaaS) dramatically decreases the barrier of entry to machine learning through accessible, externally trained model building and deployment. However, numerous studies have shown that MLaaS models are vulnerable to ...

Many studies have shown that humans are "predictably irrational'': they do not act in a fully rational way, but their deviations from rational behavior are quite systematic. Our goal is to see the extent to which we can explain and justify these ...

Stackelberg games are a canonical model for strategic principal-agent interactions. Consider, for instance, a defense system that distributes its security resources across high-risk targets prior to attacks being executed; or a tax policymaker who sets ...

In adversarial patrolling games, a mobile Defender strives to discover intrusions at vulnerable targets initiated by an Attacker.The Attacker's utility is traditionally defined as the probability of completing an attack, possibly weighted by target ...

We model the behavioral biases of human decision-making in securing interdependent systems and show that such behavioral decision-making leads to a suboptimal pattern of resource allocation compared to non-behavioral (rational) decision-making. We ...

In this paper we introduce a generic approach to solving Sequential Security Games (SGs) based on Evolutionary Algorithms. The method (named EASG) is general and largely game-independent, which allows for its application to a wide range of SGs with only ...

Recent advances in fields such as computer vision and natural language processing have created new opportunities for developing agents that can automatically interpret their environment. Concurrently, advances in artificial intelligence have made the ...

The paper introduces a generic approach to solving Sequential Security Games (SGs) which utilizes Evolutionary Algorithms (EAs). Formulation of the method (named EASG) is general and largely game-independent, which allows for its application to a wide ...

In this paper, we investigate the importance of a defense system's learning rates to fight against the self-propagating class of malware such as worms and bots. To this end, we introduce a new propagation model based on the interactions between an ...

Deep reinforcement learning (RL) is a powerful method for generating policies in complex environments, and recent breakthroughs in game-playing have leveraged deep RL as part of an iterative multiagent search process. We build on such developments and ...

Recent advances in fields such as computer vision and natural language processing have paved the way for developing agents capable of automatically interpreting their surrounding environment. Concurrently, advances in artificial intelligence have made ...

In the real world, real-time data are now widely available, especially in security domains. Security cameras, aerial imagery, and even social media keep defenders informed when protecting important events, locations, and people. Further, advances in ...

Mobile sensors, e.g., unmanned aerial vehicles (UAVs), are becoming increasingly important in security domains and can be used for tasks such as searching for poachers in conservation areas. Such mobile sensors augment human patrollers by assisting in ...

In Stackelberg security games, a defender seeks to randomly allocate limited security resources to protect critical targets from an attack. In this paper, we study a fundamental, yet underexplored, phenomenon in security games, which we term the Curse ...

Strong Stackelberg equilibrium (SSE) is the standard solution concept of Stackelberg security games. The SSE assumes that the follower breaks ties in favor of the leader and this is widely acknowledged and justified by the assertion that the defender ...

Significant research effort in security games has focused in devising strategies that perform well even when the attacker deviates from optimal (rational) behavior. In most of these frameworks, a price needs to be paid to ensure robustness against this ...

An important way cyber adversaries find vulnerabilities in modern networks is through reconnaissance, in which they attempt to identify configuration specifics of network hosts. To increase uncertainty of adversarial reconnaissance, the network ...

Stackelberg security games have received much attention in recent years. While most existing work focuses on single-defender settings, there are many real-world scenarios that involve multiple defenders (e.g., multi-national anti-crime actions in ...

The spread of unwanted or malicious content through social media has become a major challenge. Traditional examples of this include social network spam, but an important new concern is the propagation of fake news through social media. A common approach ...