David Whyte
Carleton University
Canada

Paul C. van Oorschot
Carleton University
Canada

Evangelos Kranakis
Carleton University
Canada

Signature-based schemes for detecting Internet worms often fail on zero-day worms, and their ability to rapidly react to new threats is typically limited by the requirement of some form of human involvement to formulate updated attack signatures. We propose an anomaly-based detection technique which is the first publication in the open literature (to our knowledge) detailing a method to detect propagation of scanning worms within individual network cells, thus protecting internal networks from infection by internal clients. Our software implementation indicates that this technique is both accurate and rapid enough to enable automatic containment and suppression of worm propagation within a network cell. Our approach relies on an aggregate anomaly score, derived from the correlation of Address Resolution Protocol (ARP) activity from individual network attached devices. Our preliminary analysis and prototype indicate that this technique can be used to rapidly detect zero-day worms within a very small number of scans. The necessary individual ARP activity system profiles are automatically generated during a training period and thus the software can be rapidly deployed with minimal tuning and administration.

Keywords: Intrusion Detection, Scanning Worms

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