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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 23/2020

30.04.2020 | S.I. : Emerging applications of Deep Learning and Spiking ANN

A semi-self-taught network intrusion detection system

verfasst von: Feng Zhao, Hao Zhang, Jia Peng, Xiaohong Zhuang, Sang-Gyun Na

Erschienen in: Neural Computing and Applications | Ausgabe 23/2020

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Abstract

The ever increasing threat and complexity of modern cyber-attacks requires search for integrated and flexible intelligent defense mechanisms. Such approaches can provide optimal countermeasures, reliable credentials extraction and self-adjusting potential. Given the widespread scale of modern networks and the complexity of cyber-attacks, the problem of self-adaptation goes far beyond the capabilities of network Intrusion Detection Systems (IDS). The main weakness of IDS is the fact that they cannot adapt to new network conditions (“zero day” attacks). This research tries to overcome the above limitation, by introducing a Semi-supervised Discriminant Autoencoder (AUE) which combines Denoising AUEs with a heuristic method of class separation. In essence, the proposed algorithm learns to remodel the displaced specimens instead of the original ones in the super-sphere defined by their closest neighbors. The purpose is to understand the nature of an attack, based on generalized transformed features derived directly from unknown web environments and data.
Vorheriger Artikel A novel validation framework to enhance deep learning models in time-series forecasting
Nächster Artikel An adversarial semi-supervised approach for action recognition from pose information

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Metadaten
Titel
A semi-self-taught network intrusion detection system
verfasst von
Feng Zhao
Hao Zhang
Jia Peng
Xiaohong Zhuang
Sang-Gyun Na
Publikationsdatum
30.04.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 23/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-04914-7

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