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 |
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Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
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Online-Zugang: | Volltext |
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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. |
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ISSN: | 2511-2104 2511-2112 |
DOI: | 10.1007/s41870-023-01631-x |