GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics

Fadhel, Mariem Ben; Nyarko, Kofi

doi:10.1109/ciss.2019.8693024

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…Other Domains of Anomaly Detection with GANs. There are several additional application domains for anomaly detection using GANs that are less common: trajectory anomaly detection [115,61], human mobility anomaly detection [115], climate change [142], text anomaly detection [68], and software systems anomaly detection [94,34].…”

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

confidence: 99%

“…Precision, F1-score, accuracy, recall, sensitivity, equal error rate (EER), specificity, and receiver operating curve (ROC) are also frequently used as metrics in this area. San Francisco cabspotting: [115] SBHAR: [46] SD-OCT: [17] Sentence polarity: [68] ShanghAaiTech: [41,75] SIXray: [91] Spectralis OCT: [26] SWaT system: [93] SVHN: [50,62] TalkingData AdTracking: [113,67] Tennessee eastman: [16,28] Texas coast: [27] Thyroid: [132] UBA: [96] UCI: [38,126] UCSD: [21,22,37,39,41,54,64,65,122,74,75,79,90,107,109] Udacity: [56,61] UMN: [39,43,64,65,74,90,107] UNSW-NB15: [110] VIRAT: [81] WADI test-bed: [93] WOA13 month...…”

Section: Rq4: Which Type Of Data Instance and Datasets Are Most Commo...mentioning

confidence: 99%

“…In addition, GANs were initially created to generate continuous data. As a result, they have limited ability to deal with discrete data, as it hinders the backpropagation process [68]. Ben Fadhel and Nyarko [68] point out this problem and study GAN architectures suitable for discrete data.…”

Section: Future Research Directionsmentioning

confidence: 99%

“…As a result, they have limited ability to deal with discrete data, as it hinders the backpropagation process [68]. Ben Fadhel and Nyarko [68] point out this problem and study GAN architectures suitable for discrete data. Despite the promising results they report, this study is the only example of anomaly detection for discrete data in our review.…”

Section: Future Research Directionsmentioning

confidence: 99%

See 3 more Smart Citations

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

Sabuhi¹,

Zhou²,

Bezemer³

et al. 2021

Preprint

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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 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.

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

confidence: 99%

Section: Rq4: Which Type Of Data Instance and Datasets Are Most Commo...mentioning

confidence: 99%

Section: Future Research Directionsmentioning

confidence: 99%

Section: Future Research Directionsmentioning

confidence: 99%

See 2 more Smart Citations

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

show abstract

“…One or more classes are defined as normal, while the remaining ones are defined as anomalies [10,11]. Generative adversarial networks have been employed in anomaly detection, as well [12]. They are often used to increase the number of samples to improve the learning process of the anomaly detector.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Using Convolutional Neural Networks for Anomaly Detection

Monteiro¹,

Filho²

2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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Anomaly detection is an import field of study, which has many applications, e.g., fraud and disease detection. It consists of identifying non-conforming patterns regarding an expected behavior. Despite the improvements provided by deep learning techniques in several areas, their use for anomaly detection is not widespread. The main reason is the difficulty to learn discriminative models when all the information available regards one class, or the classes are highly unbalanced. We propose a new deep learning-based solution for the anomaly detection problem. It consists of a hybrid system, composed of a feature extractor and a one-class classifier. The feature extractor is a convolutional neural network, trained as a regressor to learn a predefined distribution. The classifier is the one-class support vector machine, which performs the anomaly detection by using the outputs provided by the feature extractor. We used a gearbox failure diagnosis data set to assess the performance of our proposal. We also compared our anomaly detection system with other deep learning-based techniques commonly found in the literature. Our proposal presented an average accuracy close to 0.95, outperforming techniques based on the reconstruction error and hybrid models.

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

Sabuhi¹,

Zhou

Bezemer

et al. 2021

IEEE Access

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 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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GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics

Cited by 4 publications

References 17 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

Feature Extraction Using Convolutional Neural Networks for Anomaly Detection

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

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