论文标题
使用深胶囊网定位针对电网的改变负载攻击
Localizing Load-Altering Attacks Against Power Grids Using Deep Capsule Nets
论文作者
论文摘要
最近的研究表明，僵尸网络型网络攻击可能会严重威胁电网的安全性，该攻击针对最终用户拥有的大量高战争智能电器。对这种攻击的准确检测和定位对于限制损害至关重要。为此，本文提出了一种使用胶囊网络（CNS）量身定制的新技术，该技术使用了通过相组测量单元（PMU）监控的频率和相位角度数据。鉴于胶囊的向量输出和它们之间的动态路由协议的好处，CNS可以获得准确的检测和定位性能。为了证明建议的技术的效率，我们将开发的CN与基准数据驱动的方法进行了比较，包括二维卷积神经网络（2D-CNN），一维CNN（1D-CNN），深度多层多层的CNNN（MLP）（MLP）（MLP）和支持矢量机器（SVM）。在IEEE 14-、39和57总线系统上进行仿真，考虑了各种现实世界中的问题，例如PMU延迟，嘈杂的数据和缺失的数据点。结果表明，CNS明显优于其他技术，因此使其适合上述网络安全应用程序。
Recent research has shown that the security of power grids can be seriously threatened by botnet-type cyber attacks that target a large number of high-wattage smart electrical appliances owned by end-users. Accurate detection and localization of such attacks is of critical importance in limiting the damage. To this end, the paper proposes a novel technique using capsule networks (CNs) tailored to the power grid security application that uses the frequency and phase angle data monitored by phasor measurement units (PMUs). With the benefit of vector output from capsules and dynamic routing agreements between them, CNs can obtain accurate detection and localization performance. To demonstrate the efficiency of the suggested technique, we compare the developed CN with benchmark data-driven methodologies, including two-dimensional convolutional neural networks (2D-CNN), one-dimensional CNN (1D-CNN), deep multi-layer perceptrons (MLP), and support vector machines (SVM). Simulations are performed on IEEE 14-, 39-, and 57-bus systems, considering various real-world issues such as PMU delays, noisy data, and missing data points. The results show that CNs significantly outperform other techniques, thus making them suitable for the aforementioned cyber security applications.
