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| Man-In-The-Middle Attack Detection Based on Bayesian Belief Network |
Egwali A.O, Alile S.O
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Abstract:
Man-in-The-Middle (MiTM) attack is one of the most intimidating forms of attack on a computing device where an attack occurs without the victim having the slightest knowledge that a breach in security has occurred. These attacks are so smartly planned that they are able to elude detection from most network instruction detection systems and they are capable of penetrating sophisticated defenses. In the past, several systems have been developed to defend against MiTM attack, but they generated a lot of false negative during testing and were unable to detect Man-in-The-Middle attack and its various forms. Hence, In this paper, we proposed and simulated a Bayesian Belief Network model to predict Man-in-The-Middle attack. The model was designed using Bayes Server and tested with data collected from cyber security repository. The model had a 99% prediction accuracy.
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