Iranian Society of Cryptology The ISC International Journal of Information Security 2008-2045 Articles in Press 2026 02 22 Static Malware Detection in Windows Executables Using Deep Neural Networks and Custom Binary Features 241272 10.22042/isecure.2026.241272 EN Sajjad Rezaei Department of Computer Engineering, Isfahan University of Technology, Isfahan, Iran. Ali Fanian Department of Computer Engineering, Isfahan University of Technology, Isfahan, Iran. Journal Article The extensive use of malware targeting Windows systems, particularly through Portable Executable (PE) files, has prompted significant research into malware detection. Although many approaches have been proposed, the increasing complexity and evasiveness of modern malware continue to present substantial challenges, underscoring the need for further advancements in detection strategies. This paper introduces a static malware detection framework based on deep learning and a set of carefully engineered binary features extracted directly from raw PE files. In contrast to conventional methods that rely on metadata or dynamic analysis, our approach performs detailed parsing of file headers, section layouts, entropy levels, import/export tables, and embedded resources to form a comprehensive feature set. A deep neural network is trained on these features, with its architecture and hyperparameters fine-tuned using Bayesian optimisation. The model is evaluated on a balanced dataset of benign and malicious PE files, achieving high accuracy (98.83%) and an F1-score of 98.95%. Fully automated and independent of dynamic execution or commercial tools, the proposed solution is well-suited for deployment in real-world applications such as antivirus systems and intrusion detection platforms.  https://www.isecure-journal.com/article_241272_04f45db24e6721a93f7b35246833f2fa.pdf