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

Adel Binbusayyis; Thavavel Vaiyapuri

doi:10.1007/s10489-021-02205-9

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

Adel Binbusayyis Thavavel Vaiyapuri

Year: 2021 Journal: Applied Intelligence Vol: 51 (10)Pages: 7094-7108 Publisher: Springer Science+Business Media

DOI: 10.1007/s10489-021-02205-9

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Keywords:

Computer science Autoencoder Artificial intelligence Discriminative model Intrusion detection system Machine learning Support vector machine Feature learning Convolutional neural network Classifier (UML) Deep learning Pattern recognition (psychology) Multilayer perceptron Data mining Artificial neural network

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Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Internet Traffic Analysis and Secure E-voting

Physical Sciences → Computer Science → Artificial Intelligence

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

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

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