A Novel Combinatorial Multi-Armed Bandit Game to Identify Online the Changing Top-K Flows in Software-Defined Networks

Shu, Zhaogang; Feng, Haoxian; Taleb, Tarik; Zhang, Zhifang

doi:10.2139/ssrn.4326503

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B Evaluating Real Network Strategy1

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2023

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“…It is required to evaluate the suggested strategies in real networks [25,26]. We simulated different networks in Mininet and then propagated the Mirai bots in these networks.…”

Section: B Evaluating Real Network Strategymentioning

confidence: 99%

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

Javadpour,

Ja'fari,

Taleb

et al. 2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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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 stateof-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.

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“…It is required to evaluate the suggested strategies in real networks [25,26]. We simulated different networks in Mininet and then propagated the Mirai bots in these networks.…”

Section: B Evaluating Real Network Strategymentioning

confidence: 99%