Future smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, several research works have revealed that such AMI networks are vulnerable to different kinds of cyber attacks. In this research work, we consider one type of such cyber attacks that targets electricity theft and we propose a novel detection mechanism based on a deep machine learning approach. While existing research papers focus on shallow machine learning architectures to detect these cyber attacks, we propose a deep feedforward neural network (D-FF-NN) detector that can thwart such cyber attacks efficiently. To optimize the D-FF-NN hyper-parameters, we apply a sequential grid search technique that significantly improves the detector»s performance while reducing the associated complexity in the learning process. We carry out extensive studies to test the proposed detector based on a publicly available real load profile data of 5000 customers. The detector»s performance is investigated against a mixture of different attacks including partial reduction attacks, selective by-pass attacks, and price-based load control attacks. Our study reveals that the proposed D-FF-NN detector presents a superior performance compared with state-of-the-art detector»s that are based on shallow machine learning architectures


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