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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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| 3. |
- Shahrebabaki, Abdolreza Sabzi, et al.
(författare)
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Acoustic-to-Articulatory Mapping With Joint Optimization of Deep Speech Enhancement and Articulatory Inversion Models
- 2022
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Ingår i: IEEE/ACM transactions on audio, speech, and language processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 2329-9290. ; 30, s. 135-147
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Tidskriftsartikel (refereegranskat)abstract
- We investigate the problem of speaker independent acoustic-to-articulatory inversion (AAI) in noisy conditions within the deep neural network (DNN) framework. In contrast with recent results in the literature, we argue that a DNN vector-to-vector regression front-end for speech enhancement (DNN-SE) can play a key role in AAI when used to enhance spectral features prior to AAI back-end processing. We experimented with single- and multi-task training strategies for the DNN-SE block finding the latter to be beneficial to AAI. Furthermore, we show that coupling DNN-SE producing enhanced speech features with an AAI trained on clean speech outperforms a multi-condition AAI (AAI-MC) when tested on noisy speech. We observe a 15% relative improvement in the Pearson's correlation coefficient (PCC) between our system and AAI-MC at 0 dB signal-to-noise ratio on the Haskins corpus. Our approach also compares favourably against using a conventional DSP approach to speech enhancement (MMSE with IMCRA) in the front-end. Finally, we demonstrate the utility of articulatory inversion in a downstream speech application. We report significant WER improvements on an automatic speech recognition task in mismatched conditions based on the Wall Street Journal corpus (WSJ) when leveraging articulatory information estimated by AAI-MC system over spectral-alone speech features.
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| 4. |
- Shahrebabaki, Abdolreza Sabzi, et al.
(författare)
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Sequence-to-sequence articulatory inversion through time convolution of sub-band frequency signals
- 2020
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Ingår i: Interspeech. - Shanghai, China : The International Speech Communication Association (ISCA). ; , s. 2882-2886
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Konferensbidrag (refereegranskat)abstract
- We propose a new acoustic-to-articulatory inversion (AAI) sequence-to-sequence neural architecture, where spectral sub-bands are independently processed in time by 1-dimensional (1-D) convolutional filters of different sizes. The learned features maps are then combined and processed by a recurrent block with bi-directional long short-term memory (BLSTM) gates for preserving the smoothly varying nature of the articulatory trajectories. Our experimental evidence shows that, on a speaker dependent AAI task, in spite of the reduced number of parameters, our model demonstrates better root mean squared error (RMSE) and Pearson's correlation coefficient (PCC) than a both a BLSTM model and an FC-BLSTM model where the first stages are fully connected layers. In particular, the average RMSE goes from 1.401 when feeding the filterbank features directly into the BLSTM, to 1.328 with the FC-BLSTM model, and to 1.216 with the proposed method. Similarly, the average PCC increases from 0.859 to 0.877, and 0.895, respectively. On a speaker independent AAI task, we show that our convolutional features outperform the original filterbank features, and can be combined with phonetic features bringing independent information to the solution of the problem. To the best of the authors' knowledge, we report the best results on the given task and data.
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