### Survey on Intrusion Detection Systems based on Deep Learning

#### Abstract

The huge growth in the data and the significant advances in computer hardware technologies resulted in the new studies existence in the deep learning field, including intrusion detection. Deep learning is sub-field of Machine Learning (ML) methods that are based on learning data representations. In this paper, a detailed survey of various deep learning methods applied in IDSs is given first. Then, a deep learning classification scheme is presented and the main works that have been reported in the deep learning works is summarized. Utilizing this approach, we have provided a taxonomy survey on the available deep architectures and algorithms in these works and classify those algorithms to three classes, which are: discriminative, hybrid and generative. After that, chosen deep learning applications are reviewed in a wide range of fields of intrusion detection. Finally, popular types of datasets and frameworks are discussed.

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DOI: http://dx.doi.org/10.21533/pen.v7i3.635

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ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

This work is licensed under a Creative Commons Attribution 4.0 International License