NDLI: Learning-based anomaly detection in BGP updates

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Learning-based anomaly detection in BGP updates

Content Provider	ACM Digital Library
Author	Zhang, Jian Rexford, Jennifer Feigenbaum, Joan
Abstract	Detecting anomalous BGP-route advertisements is crucial for improving the security and robustness of the Internet's interdomain-routing system. In this paper, we propose an instance-learning framework that identifies anomalies based on deviations from the "normal" BGP-update dynamics for a given destination prefix and across prefixes. We employ wavelets for a systematic, multi-scaled analysis that avoids the "magic numbers" (e.g., for grouping related update messages) needed in previous approaches to BGP-anomaly detection. Our preliminary results show that the update dynamics are generally consistent across prefixes and time. Only a few prefixes differ from the majority, and most prefixes exhibit similar behavior across time. This small set of abnormal prefixes and time intervals may be further examined to determine the source of anomalous behavior. In particular, we observe that many of the unusual prefixes are unstable prefixes that experience frequent routing changes.
Starting Page	219
Ending Page	220
Page Count	2
File Format	PDF
ISBN	1595930264
DOI	10.1145/1080173.1080189
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-08-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Wavelets Instance-based learning Anomaly detection
Content Type	Text
Resource Type	Article

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