<![CDATA[In an era of advancing technology, breakthrough innovations, state- of-the-art hardware development, increased computing capacity through cloud computing and supercomputers, and collaboration between research institutions and industries are the primary driv- ing forces. However, this is also accompanied by the rise in malware attacks due to increasingly complex systems and the adoption of new technologies providing vulnerabilities for attackers. High pro- cessing capabilities are exploited to develop undetectable malware, while collaboration among cybercriminals is on the rise. There- fore, effective cybersecurity protection and efforts are becoming increasingly crucial. In this study, we compared GRU, Random For- est, Gradient Boosting, LSTM, and RNN models for malware attack detection, with experimental results showing that the Gradient Boosting model achieved the highest accuracy of 99.98%.]]>
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