The role of KL divergence in anomaly detection

Publication Type:
Conference Proceeding
Performance Evaluation Review, 2011, 39 (1 SPEC. ISSUE), pp. 123 - 124
Issue Date:
Filename Description Size
p123-zhang(1).pdfPublished version394.45 kB
Adobe PDF
Full metadata record
We study the role of Kullback-Leibler divergence in the framework of anomaly detection, where its abilities as a statistic underlying detection have never been investigated in depth. We give an in-principle analysis of network attack detection, showing explicitly attacks may be masked at minimal cost through 'camouflage'. We illustrate on both synthetic distributions and ones taken from real traffic.
Please use this identifier to cite or link to this item: