Results 41 to 50 of about 109,015 (219)
A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks
Sensors, 2023 Machine learning (ML) and deep learning (DL), in particular, are common tools for anomaly detection (AD). With the rapid increase in the number of Internet-connected devices, the growing desire for Internet of Things (IoT) devices in the home, on our ...
Kyle DeMedeiros +2 more
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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.
Richard Neuberg, Yixin Shi
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Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern
Information, 2019 Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user ...
Ying Zhao +6 more
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E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System
IEEE Access, 2021 Industrial Control Systems (ICS) are evolving into smart environments with increased interconnectivity by being connected to the Internet. These changes increase the likelihood of security vulnerabilities and accidents. As the risk of cyberattacks on ICS
Chanwoong Hwang, Taejin Lee
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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, S. +3 more
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Machine Learning-Driven Predictive Analytics for Real-Time Supply Chain Risk Management
Proceedings of the International Conference on Applied Innovations in ITA resilient and efficient supply chain requires real-time risk management in an increasingly volatile global marketplace. This study examines a supply chain risk management system based on machine learning-driven prediction analytics.
Nagham Ja’far Hussein +1 more
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Integrating State-of-the-Art Approaches for Anomaly Detection and Localization in the Continual Learning Setting [PDF]
, 2023 openThe significant attention surrounding the application of anomaly detection (AD) in identifying defects within industrial environments using only normal samples has prompted research and development in this area.
BUGARIC, JOVANA
core
Comparing anomaly detection methods in computer networks
, 2010 This work in progress outlines a comparison of anomaly detection methods that we are undertaking. We are comparing different types of anomaly detection methods with the purpose of achieving results covering a broad spectrum of anomalies.
Löf, Andreas +3 more
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A steam turbine anomaly detection method based on O-DAE and SVDD
Zhejiang dianli, 2023 Anomaly detection in unlabeled and highly imbalanced monitoring data is one of the most urgent to be solved and challenging industry problems. The use of autoencoders for anomaly detection is becoming more and more popular due to the powerful high ...
XU Weimin +5 more
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CoRR, 2019 Visual defect assessment is a form of anomaly detection. This is very relevant in finding faults such as cracks and markings in various surface inspection tasks like pavement and automotive parts. The task involves detection of deviation/divergence of anomalous samples from the normal ones.
Manpreet Singh Minhas, John S. Zelek
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