Prediction of Malware Threats using Machine Learning Techniques

NONE 20260105084842876 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON COMPUTER RESEARCH 1991-8755 2415-1521 Prediction of Malware Threats using Machine Learning Techniques Raed Alazaidah Faculty of Information Technology, Zarqa University, JORDAN Ghassan Samara Faculty of Information Technology, Zarqa University, JORDAN Mohammad Aljaidi Faculty of Information Technology, Zarqa University, JORDAN Mais Haj Qasem Faculty of Information Technology, Zarqa University, JORDAN Abdullah Al-Qammaz Faculty of Information Technology, Zarqa University, JORDAN Mohammad Rasmi Al-Mousa Faculty of Information Technology, Zarqa University, JORDAN Wael Hadi Faculty of Information Technology, University of Petra, JORDAN Machine learning has been used for decades to analyze vast datasets, classify and cluster data, and make predictions using algorithms. One of its top use areas is cybersecurity, where it can help detect and prevent destructive threats such as malware. The use of machine learning in cybersecurity has proven to be a powerful tool in detecting and predicting malware attacks. In recent years, the number of Internet users has greatly increased and with it the number of malware attacks. This has made predicting malware a challenge. Consequently, to date, there is still a need to examine the numerous existing MLs’ performance. This study is presented to identify the best classification model for predicting malware using two datasets and 18 different classifiers belonging to six learning strategies. 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