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Supervised Machine Learning Methods for Cyber Threat Detection Using Genetic Algorithm
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Author(s): Daniel K. Gasu (University of Ghana, Ghana), Winfred Yaokumah (University of Ghana, Ghana)and Justice Kwame Appati (University of Ghana, Ghana)
Copyright: 2023
Pages: 24
Source title:
AI and Its Convergence With Communication Technologies
Source Author(s)/Editor(s): Badar Muneer (Mehran University of Engineering and Technology, Pakistan), Faisal Karim Shaikh (Mehran University of Engineering and Technology, Pakistan), Naeem Mahoto (Mehran University of Engineering and Technology, Pakistan), Shahnawaz Talpur (Mehran University of Engineering and Technology, Pakistan)and Jordi Garcia (Universitat Politècnica de Catalunya, Spain)
DOI: 10.4018/978-1-6684-7702-1.ch002
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Abstract
Security threats continue to pose enormous challenges to network and applications security, particularly with the emerging IoT technologies and cloud computing services. Current intrusion and threat detection schemes still experience low detection rates and high rates of false alarms. In this study, an optimal set of features were extracted from CSE-CIC-IDS2018 using genetic algorithm. Machine learning algorithms, including random forest, support vector machines, logistic regression, gradient boosting, and naïve bayes were employed for classification and the results compared. Evaluation of the performance of the proposed cyber security threat detection models found random forest as the highest attacks detection with 99.99% accuracy. K-nearest neighbor achieved 99.96% while a detection accuracy of 97.39% was obtained by support vector machines. The model which used gradient boosting obtained an accuracy of 99.97%, and the logistic regression model achieved a 94.94% accuracy. The lowest accuracy rate was obtained by the naïve bayes model with a detection accuracy of 68.84%.
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