Combining visual and automated data mining for near-real-time anomaly detection and analysis in BGP

Abstract

The security of Internet routing is a major concern because attacks and errors can result in data packets not reaching their intended destination and/ or falling into the wrong hands. A key step in improving routing security is to analyze and understand it. In the past, we and other researchers have presented various visual-based, statistical-based, and signature-based methods of analyzing Internet routing data. In this paper, we describe an integration of visual and automated data mining methods for discovering and investigating anomalies in Internet routing. We show how these different components are combined in such a way as to complement each other, creating a very effective and useful analysis tool. In addition to performing analysis on archived data, our system is able to collect, process and visualize data in near-real-time. Copyright 2004 ACM.