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EWMA forecast of normal system activity for computer intrusion detection

机译：EWMA预测计算机入侵检测的正常系统活动

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摘要

Intrusions into computer systems have caused many quality/reliability problems. Detecting intrusions is an important part of assuring the quality/reliability of computer systems by quickly detecting intrusions and associated quality/reliability problems in order to take corrective actions. In this paper, we present and compare two methods of forecasting normal activities in computer systems for intrusion detection. One forecasting method uses the average of long-term normal activities as the forecast. Another forecasting method uses the EWMA (exponentially weighted moving average) one-step-ahead forecast. We use a Markov chain model to learn and predict normal activities used in the EWMA forecasting method. A forecast of normal activities is used to detect a large deviation of the observed activities from the forecast as a possible intrusion into computer systems. A Chi square distance metric is used to measure the deviation of the observed activities from the forecast of normal activities. The two forecasting methods are tested on computer audit data of normal and intrusive activities for intrusion detection. The results indicate that the Chi square distance measure with the EWMA forecasting provides better performance in intrusion detection than that with the average-based forecasting method.
机译：入侵计算机系统已引起许多质量/可靠性问题。检测入侵是通过快速检测入侵和相关的质量/可靠性问题以采取纠正措施来确保计算机系统的质量/可靠性的重要部分。在本文中，我们介绍并比较了两种预测入侵检测计算机系统中正常活动的方法。一种预测方法是使用长期正常活动的平均值作为预测。另一种预测方法是使用EWMA（指数加权移动平均值）提前预测。我们使用马尔可夫链模型来学习和预测EWMA预测方法中使用的正常活动。正常活动的预测用于检测观察到的活动与预测的较大偏差，因为这可能会入侵计算机系统。卡方距离度量用于测量观察到的活动与正常活动的预测之间的偏差。两种预测方法在正常和侵入活动的计算机审计数据上进行了测试，以进行入侵检测。结果表明，与基于平均的预测方法相比，EWMA预测的卡方距离度量在入侵检测中提供了更好的性能。

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