Results 31 to 40 of about 307,816 (329)
Detecting Relative Anomaly [PDF]
, 2017 System states that are anomalous from the perspective of a domain expert occur frequently in some anomaly detection problems. The performance of commonly used unsupervised anomaly detection methods may suffer in that setting, because they use frequency as a proxy for anomaly.
Yixin Shi +2 more
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Geometric anomaly detection in data
Proceedings of the National Academy of Sciences, 2020 Significance The problem of fitting low-dimensional manifolds to high-dimensional data has been extensively studied from both theoretical and computational perspectives. As datasets get more heterogeneous and complicated, so must the spaces that are used to approximate them.
Heather A. Harrington +6 more
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Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System
Sensors, 2021 The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals.
Hoon Ko, Kwangcheol Rim, Isabel Praça
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Spoofing Attack Detection by Anomaly Detection [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 Spoofing attacks on biometric systems can seriously compromise their practical utility. In this paper we focus on face spoofing detection. The majority of papers on spoofing attack detection formulate the problem as a two or multiclass learning task, attempting to separate normal accesses from samples of different types of spoofing attacks.
Fatemifar, Soroush +3 more
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Anomaly detection and community detection in networks
Journal of Big Data, 2022 AbstractAnomaly detection is a relevant problem in the area of data analysis. In networked systems, where individual entities interact in pairs, anomalies are observed when pattern of interactions deviates from patterns considered regular. Properly defining what regular patterns entail relies on developing expressive models for describing the observed ...
Hadiseh Safdari, Caterina De Bacco
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Machine Learning for Anomaly Detection: A Systematic Review
IEEE Access, 2021 Anomaly detection has been used for decades to identify and extract anomalous components from data. Many techniques have been used to detect anomalies. One of the increasingly significant techniques is Machine Learning (ML), which plays an important role
Ali Bou Nassif +3 more
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Anomaly detection with inexact labels [PDF]
Machine Learning, 2020 We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels.
Tomoharu Iwata +3 more
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Improving SIEM for critical SCADA water infrastructures using machine learning [PDF]
, 2019 Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems.
A Bujari +17 more
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, 2021 Anomaly detection (AD) is considered one of the important research areas that have a diverse range of application domains. Some of the anomaly detection techniques presented in the literature were specifically implemented for certain domains, whereas others were more generic.
Abu Musa, Tahani Hussein +1 more
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Multivariate Time Series Anomaly Detection Algorithm in Missing Value Scenario [PDF]
Jisuanji kexueTime series anomaly detection is an important research field in industry.Current methods of time series anomaly detection focus on anomaly detection for complete time series data,without considering the time series anomaly detection task containing ...
ZENG Zihui, LI Chaoyang, LIAO Qing
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