  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.
