Results 21 to 30 of about 533,844 (222)
Toward Practical Crowdsourcing-Based Road Anomaly Detection With Scale-Invariant Feature
Road anomaly detection with crowdsourced sensor data has become an increasingly important field of research over the last few years. Traditional ways for road anomaly detection are either threshold-based detection techniques or feature-based detection ...
Yuanyi Chen+3 more
doaj +1 more source
Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System
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
doaj +1 more source
CONDOR: A Hybrid IDS to Offer Improved Intrusion Detection [PDF]
Intrusion Detection Systems are an accepted and very useful option to monitor, and detect malicious activities. However, Intrusion Detection Systems have inherent limitations which lead to false positives and false negatives; we propose that combining ...
David J. Day+2 more
core +2 more sources
Detecting Relative Anomaly [PDF]
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
openaire +2 more sources
Spoofing Attack Detection by Anomaly Detection [PDF]
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
openaire +4 more sources
Anomaly detection and community detection in networks
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
openaire +3 more sources
E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System
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
doaj +1 more source
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
openaire +3 more sources
Machine Learning for Anomaly Detection: A Systematic Review
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
doaj +1 more source
A Survey on Explainable Anomaly Detection
In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As anomaly detection algorithms are increasingly used in safety-critical domains, providing explanations
Zhong Li+2 more
openaire +3 more sources