Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM

With the rapid advancement in network technologies, the need for cybersecurity has gained increasing momentum in recent years. As a primary defense mechanism, an intrusion detection system (IDS) is expected to adapt and secure the computing infrastructures from the ever-changing sophisticated threat...

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Veröffentlicht in:	Applied intelligence (Dordrecht, Netherlands) Jg. 51; H. 10; S. 7094 - 7108
Hauptverfasser:	Binbusayyis, Adel, Vaiyapuri, Thavavel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.10.2021 Springer Nature B.V
Schlagworte:	Ablation Artificial Intelligence CAD Classification Classifiers Communications traffic Computer aided design Computer Science Cybersecurity Decision analysis Deep learning Feature extraction Intrusion detection systems Machine learning Machines Manufacturing Mechanical Engineering Optimization Processes Reconstruction Representations Support vector machines Traffic models Deep learning Joint optimization framework Network intrusion detection Cybersecurity Feature representation learning One-class classifier OCSVM 1D convolutional autoencoder
ISSN:	0924-669X, 1573-7497
Online-Zugang:	Volltext
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