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InFilter: Predictive Ingress Filtering to Detect Spoofed IP Traffic
Columbus, Ohio, USA June 06-June 10
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDCSW.2005.78Second International Workshop on Secu ...
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Abhrajit Ghosh, Telcordia Technologies, Inc.
Larry Wong, Telcordia Technologies, Inc.
Giovanni Di Crescenzo, Telcordia Technologies, Inc.
Rajesh Talpade, Telcordia Technologies, Inc.
Cyber-attackers often use incorrect source IP addresses in attack packets (spoofed IP packets) to achieve anonymity, reduce the risk of trace-back and avoid detection. We present the predictive ingress filtering (InFilter) approach for network-based detection of spoofed IP packets near cyber-attack targets. Our InFilter hypothesis states that traffic entering an IP network from a specific source frequently uses the same ingress point. We have empirically validated this hypothesis by analysis of trace-routes to 20 Internet targets from 24 Looking-Glass sites, and 30-days of Border Gateway Protocol-derived path information for the same 20 targets. We have developed a system architecture and software implementation based on the InFilter approach that can be used at Border Routers of large IP networks to detect spoofed IP traffic. Our implementation had a detection rate of about 80% and a false positive rate of about 2% in testbed experiments using Internet traffic and real cyber-attacks.
Citation:
Abhrajit Ghosh, Larry Wong, Giovanni Di Crescenzo, Rajesh Talpade, "InFilter: Predictive Ingress Filtering to Detect Spoofed IP Traffic," icdcsw, vol. 2, pp.99-106, Second International Workshop on Security in Distributed Computing Systems (SDCS) (ICDCSW'05), 2005
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