Short Communication: An efficient negative selection algorithm with further training for anomaly detection

Authors:
Maoguo Gong;Jian Zhang;Jingjing Ma;Licheng Jiao
Affiliations:
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an 710071, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an 710071, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an 710071, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an 710071, China
Venue:
Knowledge-Based Systems
Year:
2012

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Abstract

Negative selection algorithm has been shown to be efficient for anomaly detection problems. This letter presents an improved negative selection algorithm by integrating a novel further training strategy into the training stage. The main process of further training is generating self-detectors to cover the self-region. A primary purpose of adopting further training is reducing self-samples to reduce computational cost in testing stage. It can also improve the self-region coverage. The testing stage focuses on the processing of testing samples lied within the holes. The experimental comparison among the proposed algorithm, the self-detector classification, and the V-detector on seven artificial and real-world data sets shows that the proposed algorithm can get the highest detection rate and the lowest false alarm rate in most cases.