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    A new computer security technique based data science techniques
    (İstanbul Gedik Üniversitesi, 2023) Mutar, Mohammed Hazim Mutar; Sarıkahya, Ahmet
    Due to the proliferation and dependence on digital technology, cyber security has emerged as a pressing issue in today's world. The dangers of unauthorized access, data breaches, and network invasions are growing as more people and businesses take use of the perks of networked technology. Intrusion detection is essential in reducing these dangers since it allows for instantaneous response to security breaches. Network traffic, system logs, and other data sources are monitored and analyzed by intrusion detection systems (IDS) in search of indicators of hacking or other malicious activity. In the event of a security breach or other harmful activity, these technologies can detect it and notify the appropriate authorities. The success of an IDS, however, hinges largely on the careful selection of features. The success or failure of an intrusion detection system relies heavily on the quality of the dataset used for detection, and this is where "feature selection" comes in. The objective is to minimize the data's dimensionality while maintaining or improving the features' discriminating ability. High-dimensional datasets, irrelevant or redundant features, and the curse of dimensionality are only some of the obstacles that feature selection in intrusion detection seeks to address. Manual feature selection based on expert knowledge or trial and error are examples of traditional ways; nevertheless, these methods are time-consuming, subjective, and sometimes unproductive.