Research Area: Machine Learning
Anomaly 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
Author(s) Name: Guansong Pang; Charu Aggarwal; Chunhua Shen; Nicu Sebe
Journal name: IEEE Transactions on Neural Networks and Learning Systems
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/TNNLS.2022.3162123
Volume Information: Volume: 33, Issue: 6, June 2022
