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The number of vehicles travelling on the road has increased tremendously over the past few decades throughout the globe. The advent of Intelligent Transport System (ITS) enabled the vehicles to be managed intelligently. However, the ITS network can be intruded by malicious users to access the sensitive information. So a strong Intrusion Detection System (IDS) is required to safeguard the ITS network. Due to the huge volume of data generated by the sensors in the vehicles in ITS, there is a dire need to extract the most important features from the ITS data for classification phase in IDS. Even though, many researchers have worked on building an IDS system in ITS, not much work is done on addressing the aforementioned issue. To address this problem, we have utilized the Moth–Flame Optimization (MFO) in this work. In the proposed work, a MFO based ensemble machine learning model is proposed to classify …