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Filters: Author is Liu, Xinran  [Clear All Filters]
2021-12-20
Liu, Jieling, Wang, Zhiliang, Yang, Jiahai, Wang, Bo, He, Lin, Song, Guanglei, Liu, Xinran.  2021.  Deception Maze: A Stackelberg Game-Theoretic Defense Mechanism for Intranet Threats. ICC 2021 - IEEE International Conference on Communications. :1–6.

The intranets in modern organizations are facing severe data breaches and critical resource misuses. By reusing user credentials from compromised systems, Advanced Persistent Threat (APT) attackers can move laterally within the internal network. A promising new approach called deception technology makes the network administrator (i.e., defender) able to deploy decoys to deceive the attacker in the intranet and trap him into a honeypot. Then the defender ought to reasonably allocate decoys to potentially insecure hosts. Unfortunately, existing APT-related defense resource allocation models are infeasible because of the neglect of many realistic factors.In this paper, we make the decoy deployment strategy feasible by proposing a game-theoretic model called the APT Deception Game to describe interactions between the defender and the attacker. More specifically, we decompose the decoy deployment problem into two subproblems and make the problem solvable. Considering the best response of the attacker who is aware of the defender’s deployment strategy, we provide an elitist reservation genetic algorithm to solve this game. Simulation results demonstrate the effectiveness of our deployment strategy compared with other heuristic strategies.