Deep learning for anomaly detection

• Main Authors: PANG, Guansong, AGGARWAL, Charu, SHEN, Chunhua, SEBE, Nicu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Deep learning; anomaly detection; feature extraction; learning systems; malware; modeling; Artificial Intelligence and Robotics; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/7213; https://ink.library.smu.edu.sg/context/sis_research/article/8216/viewcontent/Editorial_Deep_Learning_for_Anomaly_Detection_pvoa.pdf
• Institution: Singapore Management University

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.