{'fraction_non_alphanumeric': 0.018854748603351956, 'fraction_numerical': 0.002094972067039106, 'mean_word_length': 5.727699530516432, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
PCA-LSTM Learning Networks With Markov Chain Models for Online Classification of Cyber-Induced Outages in Power System

The existing power system utilizes communication infrastructure for fast and reliable transfer of control and protection inputs. This dependency of a power system on communication infrastructure for critical applications makes it vulnerable to cyber attacks. Cyber-induced outages trigger both randomized and intentional switchings in a power system producing relatively similar dynamics as natural events making their classification difficult. This article proposes a principal component analysis (PCA) assisted sequential deep learning approach for online classification of cyber outages and natural events in a power system. This article provides objective-driven models for false setting injection (FSI) and false command injection (FCI) type attacks. The proposed classification method uses PCA to deduce truncated z-score sequences [or principal test sequences (PTSs)] capturing distinct spatio-temporal progression patterns of natural disturbances and cyber outages. The PTSs in the training sets are shuffled and sampled using a stratified random sampling technique and classified using an ensemble long short-term memory network. The proposed method is tested for simulation examples of FSI and FCI attacks in the standard IEEE118-bus test system, where it showed improved accuracy and time performance.