Comprehensive Evaluation of Machine Learning and Deep Learning Approaches for Malware Detection - Alfema Kansha Nandita (JOURNAL SAINS STUDENT RESEARCH, 2025)
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%.