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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Baniata, Laith H.
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Computer Science & IT Engineering

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Artificial intelligence-driven method for the discovery and prevention of distributed denial of service attacks ALDabbas, Ashraf; Baniata, Laith H.; AlSaaidah, Bayan A.; Mustafa, Zaid; Alali, Muath; Rateb, Roqia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp614-628

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%.
Co-Authors Alali, Muath AlDabbas, Ashraf AlSaaidah, Bayan A. Mustafa, Zaid Rateb, Roqia