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Machine learning for Internet of things anomaly detection under low-quality data
International Journal of Distributed Sensor Networks, 2022 With the popularization of Internet of things, its network security has aroused widespread concern. Anomaly detection is one of the important technologies to protect network security.
Shangbin Han, Qianhong Wu, Yang Yang
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Anomaly detection has become one of the crucial tasks in hyperspectral images processing. However, most deep learning-based anomaly detection methods often suffer from the incapability of utilizing spatial–spectral information, which decreases the
Song Xiao +5 more
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Comparative Analysis of Anomaly Detection Techniques Using Generative Adversarial Network
Sir Syed University Research Journal of Engineering and Technology, 2023 Anomaly detection in a piece of data is a challenging task. Researchers use different approaches to classify data as anomalous. These include traditional, supervised, unsupervised, and semi-supervised techniques.
Imran Ullah Khan +4 more
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Hyperspectral Anomaly Detection Method Based on Low Rank Total Variation Regu-larization
Jisuanji kexue yu tansuo, 2020 Hyperspectral remote sensing technology provides abundant spectral information for exploring objects and supplies a better data source for anomaly detection.
XU Chao, ZHAN Tianming
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A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. [PDF]
PLoS ONE, 2016 Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of ...
Markus Goldstein, Seiichi Uchida
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Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features
, 2018 One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications.
A Zimek +15 more
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Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks
, 2018 Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the ...
Jauhar, Ahmad +3 more
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International Journal of Distributed Sensor Networks, 2016 As WSNs gain popularity, they are becoming more and more necessary for traffic anomaly detection. Because worms, attacks, intrusions, and other kinds of malicious behaviors can be recognized by traffic analysis and anomaly detection, WSN traffic anomaly ...
Qin Yu +3 more
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Utility Analysis about Log Data Anomaly Detection Based on Federated Learning
Applied Sciences, 2023 Logs that record system information are managed in anomaly detection, and more efficient anomaly detection methods have been proposed due to their increase in complexity and scale.
Tae-Ho Shin, Soo-Hyung Kim
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Hyperspectral anomaly detection: a performance comparison of existing techniques
International Journal of Digital Earth, 2022 Anomaly detection in Hyperspectral Imagery (HSI) has received considerable attention because of its potential application in several areas. Numerous anomaly detection algorithms for HSI have been proposed in the literature; however, due to the use of ...
Noman Raza Shah +6 more
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