A game-theoretic approach for channel security against active time-varying attacks based on artificial noise
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To penetrate sensitive communication systems, attackers can attack the channel using an active time-varying (ATV) way, which will lead to a great information loss. The conventional approach is to encrypt the original signal making it difficult for attackers to get information. However, this technology is constrained by the limited wireless terminal equipment. In this paper, we choose to insert artificial noise into the channel, which aims at disturbing the attackers and reducing the loss of the system once attacks occur. However, this technology would produce some side effects and there is a tradeoff between inserting artificial noise and minimizing information loss. In this paper, we deal with this issue and propose a game-theoretic framework to minimize the total losses. We model the problem as a Stackelberg security game between the attacker and the defender. Furthermore, we propose a novel method to reduce the searching space of computing the Strong Stackelberg Equilibrium which is the optimal defense strategy. This algorithm reduces a M-dimensional problem to M 1-dimensional problems so that the complexity is lowered. The experimental results show that our proposed algorithm significantly outperforms other non-strategic strategies in terms of decreasing the total losses against ATV attacks.
KeywordsActive time-varying attacks Artificial noise Stackelberg game Stackelberg equilibrium Channel security
This paper is supported by Nature Science Foundation of China under Grant nos. 61572095, 61877007. An earlier version of this paper was presented at the 13th International Conference on Future Networks and Communications.
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Conflicts of interest
The authors declare that they have no conflict of interest.
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This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
- An B, Tambe M, Sinha A (2016) Stackelberg security games (SSG) basics and application overview. Improving homeland security decisions. Cambridge University Press, CambridgeGoogle Scholar
- Conitzer V, Sandholm T (2006) Computing the optimal strategy to commit to. In: Proceedings of the 7th ACM conference on Electronic commerce. ACM, pp 82–90Google Scholar
- Gagandeep A, Kumar P (2012) Analysis of different security attacks in manets on protocol stack a—review. Int J Eng Adv Technol (IJEAT) 1(5):269–275Google Scholar
- Haskell W, Kar D, Fang F, Tambe M, Cheung S, Denicola E (2014) Robust protection of fisheries with compass. In: Twenty-Sixth IAAI conferenceGoogle Scholar
- Laszka A, Vorobeychik Y, Koutsoukos X (2015) Optimal personalized filtering against spear-phishing attacks. In: Twenty-Ninth AAAI conference on artificial intelligenceGoogle Scholar
- Li X, Li S, Hao J, Feng Z, An B (2017) Optimal personalized defense strategy against man-in-the-middle attack. In: Thirty-First AAAI conference on artificial intelligenceGoogle Scholar
- Negi R, Goel S (2005) Secret communication using artificial noise. In: IEEE vehicular technology conference, vol. 62, p 1906 (Citeseer)Google Scholar
- Nguyen TH, Sinha A, Gholami S, Plumptre A, Joppa L, Tambe M, Driciru M, Wanyama F, Rwetsiba A, Critchlow R et al (2016) Capture: a new predictive anti-poaching tool for wildlife protection. In: Proceedings of the 2016 international conference on autonomous agents & multiagent systems. International foundation for autonomous agents and multiagent systems, pp 767–775Google Scholar
- Yang Q, Chen KF (2006) Information hiding capacity in unknown channel. Comput Simul 23(3):104–106Google Scholar
- Zhang J, Yin J, Xu T, Gao Z, Qi H, Yin H (2018) The optimal game model of energy consumption for nodes cooperation in WSN. J Ambient Intell Hum Comput 2018:1–11Google Scholar