Results 81 to 90 of about 109,015 (219)

Harborfront Anomaly Detection

Neural Processing Letters
Abstract Creating high-quality datasets for the task of video anomaly detection is challenging due to a subjective anomaly definition and the rarity of anomalies, which oust the possibility of obtaining statistically significant data. This results in datasets where anomalies are placed in a single category, and are often considered less ...
Jacob V. Dueholm, Mia Sandra Nicole Siemon, Radu Tudor Ionescu, Thomas B. Moeslund, Kamal Nasrollahi   +4 more
openaire   +2 more sources

Counterexample Explanation by Anomaly Detection [PDF]

, 2012
Since counterexamples generated by model checking tools are only symptoms of faults in the model, a significant amount of manual work is required in order to locate the fault that is the root cause for the presence of counterexamples in the model. In this paper, we propose an automated method for explaining counterexamples that are symptoms of the ...
Leue, Stefan, Tabaei Befrouei, Mitra
openaire   +2 more sources

Quantitative benchmarking of anomaly detection methods in digital pathology images

Machine Learning: Health
Anomaly detection has been widely studied in the context of industrial defect inspection, with numerous methods developed to tackle a range of challenges.
Can Cui, Xindong Zheng, Ruining Deng, Quan Liu, Tianyuan Yao, Keith T Wilson, Lori A Coburn, Bennett A Landman, Haichun Yang, Yaohong Wang, Yuankai Huo   +10 more
doaj   +1 more source

Evolving discrete-valued anomaly detectors for a network intrusion detection system using negative selection

, 2006
Network intrusion detection is the problem of detecting unauthorised use of, or access to, computer systems over a network. One approach is anomaly detection, where deviations from a model of normal network activity are reported.
Powers, Simon T., He, Jun
core  

Multi-Task Network Anomaly Detection using Federated Learning

, 2019
Because of the complexity of network traffic, there are various significant challenges in the network anomaly detection fields. One of the major challenges is the lack of labeled training data.
Jian Teng, Ying Zhao, Di Wu, Yu, Shui, Chen, Junjun, Shui Yu, Wu, Di, Teng, Jian, Junjun Chen, Zhao, Ying   +9 more
core   +1 more source

LA-EAD: Simple and Effective Methods for Improving Logical Anomaly Detection Capability

Sensors
In the field of intelligent manufacturing, image anomaly detection plays a pivotal role in automated product quality inspection. Most existing anomaly detection methods are adept at capturing local features of images, achieving high detection accuracy ...
Zhixing Li, Zan Yang, Lijie Zhang, Lie Yang, Jiansheng Liu   +4 more
doaj   +1 more source

ViGLAD: Vision Graph Neural Networks for Logical Anomaly Detection

IEEE Access
Quality inspection is an industrial field with a growing interest in anomaly detection research. An anomaly in an image can either be structural or logical.
Firas Zoghlami, Dena Bazazian, Giovanni L. Masala, Mario Gianni, Asiya Khan   +4 more
doaj   +1 more source

Enhancing network traffic prediction and anomaly detection via statistical network traffic separation and combination strategies

, 2006
In this paper, we propose, study and analyze a new network traffic prediction methodology, based on the \u27frequency domain\u27 traffic analysis and filtering, with the objective of enhancing the network anomaly detection capabilities.
Jiang, J, Papavassiliou, S, Papavassiliou, Symeon, Jiang, Jun   +3 more
core   +1 more source

Anomaly Detection Algorithms for Real-Time Log Data Analysis at Scale

IEEE Access
In recent years, Artificial Intelligence for IT Operations (AIOps) has gained popularity as a solution to various challenges in IT operations, particularly in anomaly detection.
Andras Horvath, Andras Olah, Attila Pinter, Balint Siklosi, Gergely Lukacs, Istvan Z. Reguly, Kalman Tornai, Tamas Zsedrovits, Zoltan Mathe   +8 more
doaj   +1 more source

Research on traffic representation in network anomaly detection

Tongxin xuebao
Aiming to address the problem of information loss in traffic representation for network anomaly detection, the impact of feature information dimension of different traffic representation on anomaly detection performance was analyzed from the perspective ...
SUN Jianwen, ZHANG Bin, CHANG Heyu
doaj