Artificial intelligence-driven method for the discovery and prevention of distributed denial of service attacks

Abstract

Distributed denial of service (DDoS) attacks has emerged as a prominent cyber threat in contemporary times. By impeding the machine's capacity to give services to legitimate clients, the impacted system performance and buffer size are reduced. Researchers are working to build sophisticated algorithms that can identify and thwart DDoS violations. An effective approach for DDoS attacks has been proposed in this work. This research presents a model as a potential explanation for DDoS assaults. In order to successfully identify this kind of attacks, which may stop or block the urgent and vital transmission of data, we present a distinctive method that integrates a pair of fully connected layers within an amalgamated deep learning (DL) framework with long short-term memory (LSTM) and a max pooling layer. The acquired accuracy reached 99.58%.