电网需求操纵攻击的检测

Srinidhi Madabhushi, Rinku Dewri
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引用次数: 5

摘要

全球物联网设备使用量的增加对电网构成了威胁。当攻击者可以访问同一地理位置内的多个物联网设备时,他们可能会通过调节高瓦数物联网设备的僵尸网络来破坏电网。在这种攻击期间,异常检测可以方便地通知电源操作员异常行为。然而,当攻击发生的时间较长且模糊不清时,很难检测到异常情况。为了有效检测此类攻击,我们提出了一种新的动态阈值机制,该机制与基于预测的异常评分技术相结合。我们将我们的检测率与预定义的阈值机制和商业检测方法进行了比较,并观察到我们的方法在我们生成的不同攻击中将检测率提高了97%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of Demand Manipulation Attacks on a Power Grid
An increased usage in IoT devices across the globe has posed a threat to the power grid. When an attacker has access to multiple IoT devices within the same geographical location, they can possibly disrupt the power grid by regulating a botnet of high-wattage IoT devices. Anomaly detection comes handy to inform the power operator of an anomalous behavior during such an attack. However, it is difficult to detect anomalies when attacks take place obscurely and for prolonged time periods. To effectively detect such attacks, we propose a novel dynamic thresholding mechanism that is used with prediction-based anomaly score techniques. We compare our detection rates to predefined thresholding mechanisms and commercial detection methods and observe that our method improves the detection rate up to 97% across different attacks that we generate.
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