基于多马尔可夫模型的BGP数据异常检测

2009 DoD High Performance Computing Modernization Program Users Group Conference Pub Date : 2009-06-15 DOI:10.1109/HPCMP-UGC.2009.61

Judith D. Gardiner

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引用次数: 3

摘要

该项目探索了一种早期检测互联网干扰的新机制，包括自然和恶意事件。我们使用多个隐马尔可夫模型来分析一种称为边界网关协议(BGP)的全局路由数据。在平静期和干扰期之间实现了相当好的区分，在自然事件和恶意事件之间实现了一定的区分。这个项目本质上是探索性的;没有对结果进行验证。

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Multiple Markov Models for Detecting Internet Anomalies from BGP Data

This project explores a new mechanism for early detection of Internet disturbances, including both natural and malicious events. We used multiple hidden Markov models to analyze a type of global routing data called Border Gateway Protocol (BGP). Reasonably good discrimination was achieved between quiet periods and disturbances, and some discrimination was achieved between natural and malicious events. This project was exploratory in nature; no validation has been done on the results.

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2009 DoD High Performance Computing Modernization Program Users Group Conference

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