Markov Decision Process for Modeling Social Engineering Attacks and Finding Optimal Attack Strategies

Abstract

It is important to comprehend the attacker’s behavior and capacity in order to build a stronger fortress and thus be able to protect valuable assets more effectively. Prior to launching technical and physical attacks, an attacker may enter the reconnaissance stage and gather sensitive information. To collect such valuable data, one of the most effective approaches is through conducting social engineering attacks, borrowing techniques from deception theory. As a result, it is of utmost importance to understand when an attacker behaves truthfully and when the attacker opts to be deceitful. This paper models attacker’s states using Markov Decision Process (MDP) and studies the attacker’s decision for launching deception attacks in terms of cooperation and deception costs. The study is performed through Markov Decision Process (MDP) modeling, where the states of attackers are modeled along with the permissible actions that can be taken. We found that the optimal policy regarding being deceitful or truthful depends on the cost associated with deception and how much the attacker can afford to take the risk of launching deception attacks. More specifically, we observed that when the cost of cooperation is low (e.g., 10%), by taking MDP optimal policy, the attacker cooperates with the victim as much as possible in order to gain their trust; whereas, when the cost of cooperation is high (e.g., 50%), the attacker takes deceptive action earlier in order to minimize the cost of interactions while maximizing the impact of the attack. We report four case studies and simulations through which we demonstrate the trade-off between cooperative and deceptive actions in accordance with their costs to attackers.