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ECVision indexed and annotated bibliography of cognitive computer vision publications This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1 The complete text version of this BibTeX file is available here: ECVision_bibliography.bib Y. Liu and K. Chen and X. Liao and W. Zhang A genetic clustering method for intrusion detection ABSTRACT 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-learning 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.

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