A genetic clustering method for intrusion detection

Traditional intrusion detection methods lack extensibility in face of changing network configurations as well as adaptability in face of unknown attack types. Meanwhile, current machine-leaming algorithms need labeled data for training first, so they are computational expensive and sometimes misled by artificial data. In this paper, a new detection algorithm, the Intrusion Detection Based on Genetic Clustering (IDBGC) algorithm, is proposed. It can automatically establish clusters and detect intruders by labeling normal and abnormal groups. Computer simulations show that this algorithm is effective for intrusion detection. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.