Learning-based anomaly detection in BGP updates

Jian Zhang, J. Rexford, J. Feigenbaum
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引用次数: 70

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.
基于学习的BGP更新异常检测
检测异常bgp路由通告对于提高Internet域间路由系统的安全性和鲁棒性至关重要。在本文中,我们提出了一个实例学习框架,该框架基于对给定目的地前缀和跨前缀的“正常”bgp更新动态的偏差来识别异常。我们使用小波进行系统的、多尺度的分析,避免了以前的bp异常检测方法中需要的“幻数”(例如,用于分组相关更新消息)。我们的初步结果表明,更新动态在不同的前缀和时间通常是一致的。只有少数前缀与大多数前缀不同,大多数前缀在不同时间表现出相似的行为。这一小组异常前缀和时间间隔可以进一步检查,以确定异常行为的来源。特别是,我们观察到许多不寻常的前缀都是经历频繁路由更改的不稳定前缀。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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