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  1. Cybersecurity Fusion: Leveraging Mafia Game Tactics and Reinforcement Learning for Botnet Detection
Cybersecurity Fusion: Leveraging Mafia Game Tactics and Reinforcement Learning for Botnet Detection
82 viewed

Cybersecurity Fusion: Leveraging Mafia Game Tactics and Reinforcement Learning for Botnet Detection

Javadpour, A

Cybersecurity Fusion: Leveraging Mafia Game Tactics and Reinforcement Learning for Botnet Detection

Javadpour, A ; Sharif University of Technology | 2023

82 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/GLOBECOM54140.2023.10437968
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2023
  4. Abstract:
  5. Mafia, also known as Werewolf, is a game of uncertainty between two teams, which aims to eliminate the other team's players from the game. The similarities between detecting the Mafia members in this game and botnet detection in a computer network motivate us to solve the botnet detection problem using this game's winning strategies. None of the state-of-the-art researches have used the Mafia game strategies to detect the network's malicious nodes. In this paper, we first propose the Mafia detection strategies, which are applied using linear relation and reinforcement learning techniques. We then use the suggested strategies in a network infected by the Mirai botnet, using Mininet, to evaluate the performance of botnet detection. The average results show that the suggested strategies are 11% more accurate than the existing ones for the Mafia game. Additionally, the true positive and true negative detection rates of a network modeled by the proposed Mafia game are 71% and 91%, respectively. © 2023 IEEE
  6. Keywords:
  7. Botnet detection ; Cybersecurity ; Distributed denial of service (DDoS) attacks ; Mafia game ; Network security ; Reinforcement learning
  8. Source: Proceedings - IEEE Global Communications Conference, GLOBECOM ; 2023 , Pages 6005-6011 ; 23340983 (ISSN); 979-835031090-0 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/10437968