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Recent research has shown that the security of power grids can be seriouslythreatened by botnet-type cyber attacks that target a large number ofhigh-wattage smart electrical appliances owned by end-users. Accurate detectionand localization of such attacks is of critical importance in limiting thedamage. To this end, the paper proposes a novel technique using capsulenetworks (CNs) tailored to the power grid security application that uses thefrequency and phase angle data monitored by phasor measurement units (PMUs).With the benefit of vector output from capsules and dynamic routing agreementsbetween them, CNs can obtain accurate detection and localization performance.To demonstrate the efficiency of the suggested technique, we compare thedeveloped CN with benchmark data-driven methodologies, includingtwo-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, andmissing data points. The results show that CNs significantly outperform othertechniques, thus making them suitable for the aforementioned cyber securityapplications.

Localizing Load-Altering Attacks Against Power Grids Using Deep Capsule Nets