Khaled Soliman1,*, Mohamed Sobh2, Ayman M. Bahaa-Eldin2
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1419-1439, 2024, DOI:10.32604/cmc.2024.048883
Abstract The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leads to wide losses for various organizations. These dangers have proven that signature-based approaches are insufficient to prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious Executable Detection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE) files in hosts using Windows operating systems through collecting PE headers and applying machine learning mechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031 benign files and 179,071 malware samples from diverse sources to ensure the efficiency… More >
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