Abstract:
DDoS attacks are attacks executed in stages with the sole aim of consciously restricting computing devices that receives from and provide services, resources to other devices on the network from exercising its responsibility as a client, service and resource provider by continuously engaging these computing devices with data sent simultaneously from advanced persistent threat (APT) devices. These attacks are so smartly designed that they are able to evade detection from most network instruction detection systems and they are capable of infiltrating complicated defenses. In this paper, we proposed and simulated a Bayesian Belief Network Model to predict DDoS Attacks with IP Information. The model was designed using Bayes Server and tested with data collected from cyber security repository. The model had a 99.47% prediction accuracy.
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