Information-theoretic measures for anomaly detection

Wenke Lee, Dong Xiang
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引用次数: 638

Abstract

Anomaly detection is an essential component of protection mechanisms against novel attacks. We propose to use several information-theoretic measures, namely, entropy, conditional entropy, relative conditional entropy, information gain, and information cost for anomaly detection. These measures can be used to describe the characteristics of an audit data set, suggest the appropriate anomaly detection model(s) to be built, and explain the performance of the model(s). We use case studies on Unix system call data, BSM data, and network tcpdump data to illustrate the utilities of these measures.
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异常检测的信息论方法
异常检测是防范新型攻击的重要组成部分。我们建议使用几个信息理论度量,即熵、条件熵、相对条件熵、信息增益和信息成本来进行异常检测。这些度量可用于描述审计数据集的特征,建议要构建的适当异常检测模型,并解释模型的性能。我们使用Unix系统调用数据、BSM数据和网络tcpdump数据的案例研究来说明这些度量的实用程序。
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