Honeypot Allocation for Cyber Deception in Dynamic Tactical Networks: A Game Theoretic Approach (2308.11817v3)
Abstract: Honeypots play a crucial role in implementing various cyber deception techniques as they possess the capability to divert attackers away from valuable assets. Careful strategic placement of honeypots in networks should consider not only network aspects but also attackers' preferences. The allocation of honeypots in tactical networks under network mobility is of great interest. To achieve this objective, we present a game-theoretic approach that generates optimal honeypot allocation strategies within an attack/defense scenario. Our proposed approach takes into consideration the changes in network connectivity. In particular, we introduce a two-player dynamic game model that explicitly incorporates the future state evolution resulting from changes in network connectivity. The defender's objective is twofold: to maximize the likelihood of the attacker hitting a honeypot and to minimize the cost associated with deception and reconfiguration due to changes in network topology. We present an iterative algorithm to find Nash equilibrium strategies and analyze the scalability of the algorithm. Finally, we validate our approach and present numerical results based on simulations, demonstrating that our game model successfully enhances network security. Additionally, we have proposed additional enhancements to improve the scalability of the proposed approach.
- Mandiant Intelligence Center. Apt1: Exposing one of china’s cyber espionage units. Mandian. com, 2013.
- Dynamic connectivity game for adversarial internet of battlefield things systems. IEEE Internet of Things Journal, 5(1):378–390, 2017.
- Key challenges of military tactical networking and the elusive promise of manet technology. IEEE Communications Magazine, 44(11):39–45, 2006.
- Software defined networking: State of the art and research challenges. Computer Networks, 72:74–98, 2014.
- National Science Foundation. Advances in computer mobility, connectivity and networks. https://new.nsf.gov/news/advances-computer-mobility-connectivity-networks. Accessed: Jan, 2023.
- Cyber deception: Overview and the road ahead. IEEE Security & Privacy, 16(2):80–85, 2018.
- Honeypots: concepts, approaches, and challenges. In Proceedings of the 45th annual southeast regional conference, pages 321–326, 2007.
- Michael L Littman. Markov games as a framework for multi-agent reinforcement learning. In Machine learning proceedings 1994, pages 157–163. Elsevier, 1994.
- A review of attack graph and attack tree visual syntax in cyber security. Computer Science Review, 35:100219, 2020.
- A scalable approach to attack graph generation. In Proceedings of the 13th ACM conference on Computer and communications security, pages 336–345, 2006.
- Game theoretic cyber deception to foil adversarial network reconnaissance. In Adaptive Autonomous Secure Cyber Systems, pages 183–204. Springer, 2020.
- Conflict Analysis: Models and Resolutions. North-Holland, 1984.
- Foureye: Defensive deception against advanced persistent threats via hypergame theory. IEEE Transactions on Network and Service Management, 2021.
- Cyber deception against zero-day attacks: a game theoretic approach. In International Conference on Decision and Game Theory for Security, pages 44–63. Springer, 2022.
- A survey of defensive deception: Approaches using game theory and machine learning. IEEE Communications Surveys & Tutorials, 23(4):2460–2493, 2021.
- Machine learning approaches for predicting suicidal behaviors among university students in bangladesh during the covid-19 pandemic: A cross-sectional study. Medicine, 102(28), 2023.
- Predicting the outcome of english premier league matches using machine learning. In 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI), pages 1–6. IEEE, 2020.
- Cyber deception for computer and network security: Survey and challenges. arXiv preprint arXiv:2007.14497, 2020.
- On defensive cyber deception: A case study using sdn. In MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM), pages 110–115. IEEE, 2018.
- Gathering threat intelligence through computer network deception. In 2016 IEEE Symposium on Technologies for Homeland Security (HST), pages 1–6. IEEE, 2016.
- Understanding dhaka city traffic intensity and traffic expansion using gravity model. In 2017 20th International Conference of Computer and Information Technology (ICCIT), pages 1–6. IEEE, 2017.
- Human mobility in opportunistic networks: Characteristics, models and prediction methods. Journal of Network and Computer Applications, 42:45–58, 2014.
- Characteristics of common mobility models for opportunistic networks. In Proceedings of the 2nd ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks, pages 105–109, 2007.
- Exploring the impact of node mobility on cascading failures in spatial networks. Information Sciences, 576:140–156, 2021.
- Mitigating the impact of node mobility using mobile backbone for heterogeneous manets. Computer Communications, 35(10):1217–1230, 2012.
- The impact of node velocity diversity on mobile opportunistic network performance. Journal of Network and Computer Applications, 55:47–58, 2015.
- Effects of node mobility on energy balancing in wireless networks. Computers & Electrical Engineering, 41:314–324, 2015.
- Technologies to enable cyber deception. In 2017 International Carnahan Conference on Security Technology (ICCST), pages 1–6. IEEE, 2017.
- Optimal defense policies for partially observable spreading processes on bayesian attack graphs. In Proceedings of the second ACM workshop on moving target defense, pages 67–76, 2015.
- Dynamic noncooperative game theory. SIAM, 1998.