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A step-by-step training method for multi generator GANs with application to anomaly detection and cybersecurity
NTNU, Dept Elect Syst, Trondheim, Norway.;Monash Univ, Dept Human Centred Comp, Melbourne, Australia..
NTNU, Dept Elect Syst, Trondheim, Norway..
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH. NTNU, Dept Elect Syst, Trondheim, Norway..ORCID iD: 0000-0002-3323-5311
2023 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 537, p. 296-308Article in journal (Refereed) Published
Abstract [en]

Cyber attacks and anomaly detection are problems where the data is often highly unbalanced towards normal observations. Furthermore, the anomalies observed in real applications may be significantly different from the ones contained in the training data. It is, therefore, desirable to study methods that are able to detect anomalies only based on the distribution of the normal data. To address this problem, we propose a novel objective function for generative adversarial networks (GANs), referred to as STEPGAN. STEP-GAN simulates the distribution of possible anomalies by learning a modified version of the distribution of the task-specific normal data. It leverages multiple generators in a step-by-step interaction with a discriminator in order to capture different modes in the data distribution. The discriminator is optimized to distinguish not only between normal data and anomalies but also between the different generators, thus encouraging each generator to model a different mode in the distribution. This reduces the well-known mode collapse problem in GAN models considerably. We tested our method in the areas of power systems and network traffic control systems (NTCSs) using two publicly available highly imbalanced datasets, ICS (Industrial Control System) security dataset and UNSW-NB15, respectively. In both application domains, STEP-GAN outperforms the state-of-the-art systems as well as the two baseline systems we implemented as a comparison. In order to assess the generality of our model, additional experiments were carried out on seven real-world numerical datasets for anomaly detection in a variety of domains. In all datasets, the number of normal samples is significantly more than that of abnormal samples. Experimental results show that STEP-GAN outperforms several semi-supervised methods while being competitive with supervised methods.

Place, publisher, year, edition, pages
Elsevier BV , 2023. Vol. 537, p. 296-308
Keywords [en]
Anomaly detection, One -class classification, GAN, Mode collapse, Cyber security
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-327437DOI: 10.1016/j.neucom.2023.03.056ISI: 000978367500001Scopus ID: 2-s2.0-85151669864OAI: oai:DiVA.org:kth-327437DiVA, id: diva2:1760023
Note

QC 20230529

Available from: 2023-05-29 Created: 2023-05-29 Last updated: 2023-05-29Bibliographically approved

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Salvi, Giampiero

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