Biblio

To improve cyber defense, researchers have developed algorithms to allocate limited defense resources optimally. Through signaling theory, we have learned that it is possible to trick the human mind when using deceptive signals. The present work is an initial step towards developing a psychological theory of cyber deception. We use simulations to investigate how humans might make decisions under various conditions of deceptive signals in cyber-attack scenarios. We created an Instance-Based Learning (IBL) model of the attacker decisions using the ACT-R cognitive architecture. We ran simulations against the optimal deceptive signaling algorithm and against four alternative deceptive signal schemes. Our results show that the optimal deceptive algorithm is more effective at reducing the probability of attack and protecting assets compared to other signaling conditions, but it is not perfect. These results shed some light on the expected effectiveness of deceptive signals for defense. The implications of these findings are discussed.

The rapid increase in cybercrime, causing a reported annual economic loss of $600 billion [20], has prompted a critical need for effective cyber defense. Strategic criminals conduct network reconnaissance prior to executing attacks to avoid detection and establish situational awareness via scanning and fingerprinting tools. Cyber deception attempts to foil these reconnaissance efforts; by disguising network and system attributes, among several other techniques. Cyber Deception Games (CDG) is a game-theoretic model for optimizing strategic deception, and can apply to various deception methods. Recently introduced initial model for CDGs assumes zero-sum payoffs, implying directly conflicting attacker motives, and perfect defender knowledge on attacker preferences. These unrealistic assumptions are fundamental limitations of the initial zero-sum model, which we address by proposing a general-sum model that can also handle uncertainty in the defender’s knowledge.

Defender-attacker Stackelberg security games (SSGs) have been applied for solving many real-world security problems. Recent work in SSGs has incorporated a deceptive signaling scheme into the SSG model, where the defender strategically reveals information about her defensive strategy to the attacker, in order to influence the attacker’s decision making for the defender’s own benefit. In this work, we study the problem of signaling in security games against a boundedly rational attacker. 

To improve cyber defense, researchers have developed algorithms to allocate limited defense resources optimally. Through signaling theory, we have learned that it is possible to trick the human mind when using deceptive signals. The present work is an initial step towards developing a psychological theory of cyber deception. We use simulations to investigate how humans might make decisions under various conditions of deceptive signals in cyber-attack scenarios. We created an Instance-Based Learning (IBL) model of the attacker decisions using the ACT-R cognitive architecture. We ran simulations against the optimal deceptive signaling algorithm and against four alternative deceptive signal schemes. Our results show that the optimal deceptive algorithm is more effective at reducing the probability of attack and protecting assets compared to other signaling conditions, but it is not perfect. These results shed some light on the expected effectiveness of deceptive signals for defense. The implications of these findings are discussed.