LogGAN: A Sequence-Based Generative Adversarial Network for Anomaly Detection Based on System Logs

2019 ◽  
pp. 61-76 ◽  
Author(s):  
Bin Xia ◽  
Junjie Yin ◽  
Jian Xu ◽  
Yun Li
Keyword(s):  
Anomaly Detection ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
System Logs

NM-GAN: Noise-modulated generative adversarial network for video anomaly detection

2021 ◽  
Vol 116 ◽  
pp. 107969
Author(s):  
Dongyue Chen ◽  
Lingyi Yue ◽  
Xingya Chang ◽  
Ming Xu ◽  
Tong Jia
Keyword(s):  
Anomaly Detection ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Anomaly Detection in Particulate Matter Sensor Using Hypothesis Pruning Generative Adversarial Network

2020 ◽  
Author(s):  
Yeong Hyeon Park ◽  
Won Seok Park ◽  
Yeong Beom Kim
Keyword(s):  
Particulate Matter ◽  
Cost Effectiveness ◽  
Anomaly Detection ◽  
World Health Organization ◽  
Human Health ◽  
World Health ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Health Organization ◽  
Tapered Element Oscillating Microbalance

World Health Organization (WHO) provides the guideline for managing the Particulate Matter (PM) level because when the PM level is higher, it threats the human health. For managing PM level, the procedure for measuring PM value is needed firstly. We use Tapered Element Oscillating Microbalance (TEOM)-based PM measuring sensors because it shows higher cost-effectiveness than Beta Attenuation Monitor (BAM)-based sensor. However, TEOM-based sensor has higher probability of malfunctioning than BAM-based sensor. In this paper, we call the overall malfunction as an anomaly, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that named as Hypothesis Pruning Generative Adversarial Network (HP-GAN). We experimentally compare the several anomaly detection architectures to certify ours performing better.

scholarly journals Semi-supervised generative adversarial networks for anomaly detection

2022 ◽  
Vol 132 ◽  
pp. 01016
Author(s):  
Juan Montenegro ◽  
Yeojin Chung
Keyword(s):  
Anomaly Detection ◽  
Video Surveillance ◽  
Daily Life ◽  
Generative Adversarial Networks ◽  
Gamma Correction ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Different Types ◽  
Better Than

Advancements in security have provided ways of recording anomalies of daily life through video surveillance. For the present investigation, a semi-supervised generative adversarial network model to detect and classify different types of crimes on videos. Additionally, we intend to tackle one of the most recurring difficulties of anomaly detection: illumination. For this, we propose a light augmentation algorithm based on gamma correction to help the semi-supervised generative adversarial networks on its classification task. The proposed process performs slightly better than other proposed models.

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