Another Fuzzy Anomaly Detection System Based on Ant Clustering Algorithm

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Attacks against computer networks are evolving rapidly. Conventional intrusion detection system based on pattern matching and static signatures have a significant limitation since the signature database should be updated frequently. The unsupervised learning algorithm can overcome this limitation. Ant Clustering Algorithm (ACA) is a popular unsupervised learning algorithm to classify data into different categories. However, ACA needs to be complemented with other algorithms for the classification process. In this paper, we present a fuzzy anomaly detection system that works in two phases. In the first phase, the training phase, we propose ACA to determine clusters. In the second phase, the classification phase, we exploit a fuzzy approach by the combination of two distance based methods to detect anomalies in new monitored data. We validate our hybrid approach using the KDD Cup'99 dataset. The results indicate that, compared to several traditional and new techniques, the proposed hybrid approach achieves higher detection rate and lower false positive rate.
Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
Issue Date
2017-01
Language
English
Article Type
Article
Keywords

INTRUSION DETECTION; NETWORKS

Citation

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, v.E100A, no.1, pp.176 - 183

ISSN
1745-1337
DOI
10.1587/transfun.E100.A.176
URI
http://hdl.handle.net/10203/220944
Appears in Collection
CS-Journal Papers(저널논문)
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