A Novel LSTM-GAN Algorithm for Time Series Anomaly Detection

Zhu, Guangxuan; Zhao, Hongbo; Liu, Haoqiang; Sun, Hao

doi:10.1109/phm-qingdao46334.2019.8942842

Cited by 27 publications

(18 citation statements)

References 12 publications

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“…To address this problem, Schlegl et al proposed an additional step after training the GAN on normal data. For an image x, they proposed to find a point z in the latent space that corresponds to an image G(z), which is the most similar to the image Type of GAN List of references DCGAN [18], [22], [25], [30]- [33], [35], [40], [44], [49], [53], [55], [60], [66], [69], [77], [78], [81], [84], [86], [88], [92], [95]- [97], [99], [103]- [105], [108], [109] Standard GAN [16], [21], [24], [36], [38], [43], [45], [46], [51], [52], [54], [58], [59], [61], [70], [75], [79], [83], [87], [93], …”

Section: ) Representation Learning With Generative Adversarial Networkmentioning

confidence: 99%

“…Other primary studies evaluate their anomaly detection approach in intrusion detection, medical and image recognition domains [62], [71]. Khoshnevisian et al [52] investigate the application of their proposed GAN-based anomaly detection in medicine and on trajectory anomaly detection. Hyuk et al [111] evaluate their proposed technique for image recognition in addition to medical and trajectory anomaly detection.…”

Section: ) Data Augmentation With Generative Adversarial Networkmentioning

confidence: 99%

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Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Sabuhi¹,

Zhou

Bezemer

et al. 2021

IEEE Access

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Anomaly detection has become an indispensable tool for modern society, applied in a wide range of applications, from detecting fraudulent transactions to malignant brain tumors. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: lack of data that represents anomalous behaviour. As anomalous behaviour is usually costly (or dangerous) for a system, it is difficult to gather enough data that represents such behaviour. This, in turn, makes it difficult to develop and evaluate anomaly detection techniques. Recently, generative adversarial networks (GANs) have attracted much attention in anomaly detection research, due to their unique ability to generate new data. In this paper, we present a systematic review of the literature in this area, covering 128 papers. The goal of this review paper is to analyze the relation between anomaly detection techniques and types of GANs, to identify the most common application domains for GAN-assisted and GAN-based anomaly detection, and to assemble information on datasets and performance metrics used to assess them. Our study helps researchers and practitioners to find the most suitable GAN-assisted anomaly detection technique for their application. In addition, we present a research roadmap for future studies in this area. In summary, GANs are used in anomaly detection to address the problem of insufficient amount of data for the anomalous behaviour, either through data augmentation or representation learning. The most commonly used GAN architectures are DCGANs, standard GANs, and cGANs. The primary application domains include medicine, surveillance and intrusion detection.

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Section: ) Representation Learning With Generative Adversarial Networkmentioning

confidence: 99%

Section: ) Data Augmentation With Generative Adversarial Networkmentioning

confidence: 99%

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Sabuhi¹,

Zhou

Bezemer

et al. 2021

IEEE Access

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“…Therefore, ),+,,,: ∈ ℝ ( represents the Fourier spectrum of ./ time series signal segment in ./ measurement channels for ./ sensor. Our model takes inspiration from the model proposed by Zhu et al [29] in the detection of time series anomalies. They use the normal class data as the input of the model so that the model realizes the distinction between the normal data and anomalous data.…”

Section: Given a Fourier Spectra Datasetmentioning

confidence: 99%

A Deep Learning-Based Framework for Damage Detection With Time Series

et al. 2021

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Natural hazards have caused damages to buildings and infrastructures and economic losses in many countries. Immediate emergency response requires accurate damage detection. In recent studies, advances in deep learning approaches have put forward the development of data-driven damage detection. However, these proposed approaches can successfully classify damaged samples into different damage states without quantifying them. Therefore, we propose a deep learning-based framework for damage detection, which comprises signal processing and damage recognition modules. The signal processing module is introduced to denoise the noisy signals collected from sensors and process the denoised signals via fast Fourier transform. The damage recognition module is characterized by a novel network with the architecture of encoder-decoder-encoder. We train the network with only undamaged samples to capture the pattern in the undamaged state. In the testing stage, given an unknown damage state, we feed damaged samples into the trained network to quantify it by outputting the corresponding probability of damage. We conduct experiments to verify the validity and feasibility of the proposed framework with a dataset of a building subjected to excitations. Our approach can map samples from various damage states to a specific probability of damage, which increases as the intensity of excitation increases. INDEX TERMSEncoder-decoder-encoder, Damage detection, Deep learning, Probability of damage, Signal processing.

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“…Other primary studies evaluate their anomaly detection approach in intrusion detection, medical and image recognition domains [62,71]. Khoshnevisian et al [52] investigate the application of their proposed GAN-based anomaly detection in medicine and on trajectory anomaly detection. Hyuk et al [111] evaluate their proposed technique for image recognition in addition to medical and trajectory anomaly detection.…”

Section: Rq2: What Are the Application Domains Of Anomaly Detection W...mentioning

confidence: 99%

“…, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105] Normal and Abnormal [106, 107, 108, 109] Representation learning with GANs Type of GAN List of references DCGAN [22, 25, 31, 32, 33, 35, 40, 44, 49, 53, 55, 60, 69, 77, 81, 86, 88, 92, 95, 97, 109, 103, 104, 105, 18, 30, 66, 78, 84, 99, 108, 96] Standard GAN[16,21,24,36,38,43,45,46,51,52,54,58,59,61,70,75,79,83,87,93,94,100,107] cGAN[23,26,37,39,56,57,64,65,72,73,74,76,85,90] …”

mentioning

confidence: 99%

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Sabuhi¹,

Zhou²,

Bezemer³

et al. 2021

Preprint

View full text Add to dashboard Cite

Anomaly detection has become an indispensable tool for modern society, applied in a wide range of applications, from detecting fraudulent transactions to malignant brain tumours. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: a lack of data that represents anomalous behaviour. As anomalous behaviour is usually costly (or dangerous) for a system, it is difficult to gather enough data that represents such behaviour. This, in turn, makes it difficult to develop and evaluate anomaly detection techniques. Recently, generative adversarial networks (GANs) have attracted a great deal of attention in anomaly detection research, due to their unique ability to generate new data. In this paper, we present a systematic literature review of the applications of GANs in anomaly detection, covering 128 papers on the subject. The goal of this review paper is to analyze and summarize: (1) which anomaly detection techniques can benefit from certain types of GANs, and how, (2) in which application domains GAN-assisted anomaly detection techniques have been applied, and (3) which datasets and performance metrics have been used to evaluate these techniques. Our study helps researchers and practitioners to find the most suitable GAN-assisted anomaly detection technique for their application. In addition, we present a research roadmap for future studies in this area.

show abstract

A Novel LSTM-GAN Algorithm for Time Series Anomaly Detection

Cited by 27 publications

References 12 publications

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

A Deep Learning-Based Framework for Damage Detection With Time Series

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

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