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Cyber-Attack Detection Using Principal Component Analysis and noisy clustering algorithms: A Collaborative Machine Learning-Based Framework

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IEEE TRANSACTIONS ON SMART GRID 2022年第6期13卷 4848-4861页

作者： Parizad, Ali Hatziadoniu, Constantine Southern Illinois Univ Dept Elect & Comp Engn Carbondale IL 62901 USA Virginia Tech Adv Res Inst ARI Arlington VA 22203 USA

This paper proposes a collaborative machine learning-based framework to detect cyber-attacks in a power system, leading to deviation in the state variable behavior. Based on the proposed architecture, three different machine learning-based methods, i.e., visualization, classification, and clustering, are employed and compared to find the best one in the FDIA detection process. To this end, pre-processing is employed in the first stage. In the second stage, the patterns of the state vectors are transferred into features. Hence, 24 statistical features, including measures of central tendency, variability, measures of shape, and position, are extracted to find various properties. Then, in the third stage, a supervised algorithm is employed to rank and find the most crucial features in FDIA. In the fourth stage, an unsupervised dimensionality reduction technique (PCA) is applied to reduce the feature space. In the fifth and last stage, visualization, classification, and clustering-based methods are developed to detect FDIA. To simulate an attack, it is assumed that an intruder decreases or increases the state vectors at different buses with various attack parameters (i.e., 0.90, 0.95, 0.96, 0.97, 0.98, 1, 1.02, 1.03, 1.04, 1.05, and 1.10). The proposed method effectiveness is assessed on the New York Independent System Operator (NYISO) data applied to the IEEE 14-bus system. The results presented in the paper from different scenarios (i.e., phase angle (theta), voltage magnitude (V-m), measurements, and multiple attacks) on a real-world dataset demonstrate that the collaborative optimized PCA-Density-based machine learning technique can detect most of the attack samples with good performance scores (i.e., recall, precision, Fl) and outperforms the other investigated methods. Moreover, it is general and adaptable enough to cover the situation where either the system characteristics or the attack behavior changes.

关键词： False data injection attack (FDIA) principal component analysis noisy clustering algorithm hyper-parameter optimization adaptive semi-supervised learning detection method

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Robust Interval Type-2 FCRM algorithm for nonlinear systems identification in a stochastic environment

Robust Interval Type-2 FCRM algorithm for nonlinear systems ...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Sameh, Khadhraoui Chaari, Abdelkader Natl Higher Engn Sch Tunis Ensit Dept Elect Engn Tunis Tunisia

ISBN: (纸本)9781509059874

This paper investigates the sensibility of the interval type-2 fuzzy c-regression algorithm to noise and outliers. To overcome this problem, a modified version of this algorithm is presented. The consequences parameters of local models are estimated using the weighted recursive least squares method. This approach is tested and validated using two examples.

关键词： Type-2 fuzzy sets interval type-2 fuzzy c-regression models algorithm noisy clustering algorithm outliers weighted recursive least square

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