Publication Type

Transcript

Version

publishedVersion

Publication Date

6-2022

Abstract

A nomaly detection aims at identifying data points which are rare or significantly different from the majority of data points. Many techniques are explored to build highly efficient and effective anomaly detection systems, but they are confronted with many difficulties when dealing with complex data, such as failing to capture intricate feature interactions or extract good feature representations. Deep-learning techniques have shown very promising performance in tackling different types of complex data in a broad range of tasks/problems, including anomaly detection. To address this new trend, we organized this Special Issue on Deep Learning for Anomaly Detection to cover the latest advancements of developing deep-learning techniques specially designed for anomaly detection. This editorial note provides an overview of the paper submissions to the Special Issue, and briefly introduces each of the accepted articles.

Keywords

Deep learning, anomaly detection, feature extraction, learning systems, malware, modeling

Discipline

Artificial Intelligence and Robotics | Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Neural Networks and Learning Systems

Volume

33

Issue

6

First Page

2282

Last Page

2286

ISSN

2162-2388

Identifier

10.1109/TNNLS.2022.3162123

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1109/TNNLS.2022.3162123

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