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- Adiban, Mohammad, et al.
(författare)
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A step-by-step training method for multi generator GANs with application to anomaly detection and cybersecurity
- 2023
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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 537, s. 296-308
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Adiban, Mohammad, et al.
(författare)
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Hierarchical Residual Learning Based Vector Quantized Variational Autoencorder for Image Reconstruction and Generation
- 2022
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Ingår i: The 33<sup>rd</sup> British Machine Vision Conference Proceedings.
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Konferensbidrag (refereegranskat)abstract
- We propose a multi-layer variational autoencoder method, we call HR-VQVAE, thatlearns hierarchical discrete representations of the data. By utilizing a novel objectivefunction, each layer in HR-VQVAE learns a discrete representation of the residual fromprevious layers through a vector quantized encoder. Furthermore, the representations ateach layer are hierarchically linked to those at previous layers. We evaluate our methodon the tasks of image reconstruction and generation. Experimental results demonstratethat the discrete representations learned by HR-VQVAE enable the decoder to reconstructhigh-quality images with less distortion than the baseline methods, namely VQVAE andVQVAE-2. HR-VQVAE can also generate high-quality and diverse images that outperform state-of-the-art generative models, providing further verification of the efficiency ofthe learned representations. The hierarchical nature of HR-VQVAE i) reduces the decoding search time, making the method particularly suitable for high-load tasks and ii) allowsto increase the codebook size without incurring the codebook collapse problem.
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