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Ensemble Voting-Based anomaly detection for a Smart Grid Communication Infrastructure

Intelligent Automation & Soft Computing 2023年第6期36卷 3257-3278页

作者： Hend Alshede Laila Nassef Nahed Alowidi Etimad Fadel Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Self-Development Skills Department Common First Year DeanshipKing Saud UniversityRiyadh12211Saudi Arabia

Advanced Metering Infrastructure(AMI)is the metering network of the smart grid that enables bidirectional communications between each consumer’s premises and the provider’s control *** massive amount of data collected supports the real-time decision-making required for diverse *** communication infrastructure relies on different network types,including the *** makes the infrastructure vulnerable to various attacks,which could compromise security or have devastating ***,traditional machine learning solutions cannot adapt to the increasing complexity and diversity of *** objective of this paper is to develop an anomaly detection system(ADS)based on deep learning using the CIC-IDS2017 ***,this dataset is highly imbalanced;thus,a two-step sampling technique:random under-sampling and the Synthetic Minority Oversampling Technique(SMOTE),is proposed to balance the *** proposed system utilizes a multiple hidden layer Auto-encoder(AE)for feature extraction and dimensional *** addition,an ensemble voting based on both Random Forest(RF)and Convolu-tional Neural Network(CNN)is developed to classify the multiclass attack *** proposed system is evaluated and compared with six different state-of-the-art machine learning and deep learning algorithms:Random Forest(RF),Light Gradient Boosting Machine(LightGBM),eXtreme Gradient Boosting(XGboost),Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and bidirectional LSTM(biLSTM).Experimental results show that the proposed model enhances the detection for each attack class compared with the other machine learning and deep learning models with overall accuracy(98.29%),precision(99%),recall(98%),F_(1) score(98%),and the UNdetection rate(UND)(8%).

关键词： Advanced metering infrastructure smart grid cyberattack ensemble voting anomaly detection system CICIDS2017

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