Belief-DDoS: stepping up DDoS attack detection model using DBN algorithm

Distributed Denial of Services (DDoS) attacks severely impact various systems. Traditional approaches like signature-based and scrubbing methods remain shortcomings in detecting extensive sophisticated attacks. Thus, this paper proposes a Deep Belief Network (DBN) to construct an intelligent detecti...

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Veröffentlicht in:International journal of information technology (Singapore. Online) 2024, Vol.16 (1), p.271-278
Hauptverfasser: Wanda, Putra, Hiswati, Marselina Endah
Format: Artikel
Sprache:eng
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Zusammenfassung:Distributed Denial of Services (DDoS) attacks severely impact various systems. Traditional approaches like signature-based and scrubbing methods remain shortcomings in detecting extensive sophisticated attacks. Thus, this paper proposes a Deep Belief Network (DBN) to construct an intelligent detection model using automated feature representation. Instead of using conventional machine learning methods, we employ the DBN to train a classification model that can effectively detect DDoS attacks. Based on the experimental results, our proposed model can obtain a higher accuracy with a tiny loss.
ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-023-01631-x