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Nowadays, along with network development, due to the threats of unknown sources, information communication is more vulnerable, and thus, more secured information is required. Intrusion Detection System (IDS) is very important for cybersecurity with the presence in particular of various networked computers' foundation. An efficient IDS apply machine learning method as computational technics to increase rates of detection to gain the high accuracy and low false alarms rate within the huge amounts of data. To increase the rate of detection, researcher usually implements the optimizer. Thus, in this research, a comprehensive experimental study is presented based on various binary to optimize the rate of detection and decrease the error. Moreover, Numerous researches have been conducted about intrusion detection systems with the old dataset such as Kddcup'99 dataset, and due to this reason, most of them …