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Smart grid is an emerging technology that transfers power to users intelligently through two-way communication. Despite the benefits of this network, it is prone to different cyber-attacks. One solution to address this issue is the use of intrusion detection systems. Several studies have been conducted to investigate the shortcomings of such system, which include low detection rates and high false alarms; however, these studies did not completely address these issues. Motivated by the existing gaps, we investigate the performance of boosting-based models, namely Adaptive Boosting, Gradient Boosting, and Categorical Boosting, in detecting cyber-attacks on smart grid networks. The performance evaluation is conducted based on accuracy, probability of detection, probability of misdetection, and probability of false alarm. The results of the models were compared with those of three widely used traditional machine …