Fooling intrusion detection systems using adversarially autoencoder

Article

Chen, Junjun, Wu, Di, Zhao, Ying, Sharma, Nabin, Blumenstein, Michael and Yu, Shui. 2021. "Fooling intrusion detection systems using adversarially autoencoder." Digital Communications and Networks. 7 (3), pp. 453-460. https://doi.org/10.1016/j.dcan.2020.11.001
Article Title

Fooling intrusion detection systems using adversarially autoencoder

ERA Journal ID210373
Article CategoryArticle
AuthorsChen, Junjun, Wu, Di, Zhao, Ying, Sharma, Nabin, Blumenstein, Michael and Yu, Shui
Journal TitleDigital Communications and Networks
Journal Citation7 (3), pp. 453-460
Number of Pages8
Year2021
PublisherElsevier
Place of PublicationChina
ISSN2352-8648
Digital Object Identifier (DOI)https://doi.org/10.1016/j.dcan.2020.11.001
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S2352864820302868
Abstract

Due to the increasing cyber-attacks, various Intrusion Detection Systems (IDSs) have been proposed to identify network anomalies. Most existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows, and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network flows. Although having been used in the real world widely, the above methods are vulnerable to some types of attacks. In this paper, we propose a novel attack framework, Anti-Intrusion Detection AutoEncoder (AIDAE), to generate features to disable the IDS. In the proposed framework, an encoder transforms features into a latent space, and multiple decoders reconstruct the continuous and discrete features, respectively. Additionally, a generative adversarial network is used to learn the flexible prior distribution of the latent space. The correlation between continuous and discrete features can be kept by using the proposed training scheme. Experiments conducted on NSL-KDD, UNSW-NB15, and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.

KeywordsIntrusion detection; Cyber attacks; Autoencoder; Generative adversarial networks
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204604. Cybersecurity and privacy
4602. Artificial intelligence
Byline AffiliationsSchool of Mathematics, Physics and Computing
Centre for Future Materials
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