Asma A. Alhashmi1, Abdulbasit A. Darem1,*, Sultan M. Alanazi1, Abdullah M. Alashjaee2, Bader Aldughayfiq3, Fuad A. Ghaleb4,5, Shouki A. Ebad1, Majed A. Alanazi1
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3483-3498, 2023, DOI:10.32604/cmc.2023.041038
Abstract In an era marked by escalating cybersecurity threats, our study addresses the challenge of malware variant detection, a significant concern for a multitude of sectors including petroleum and mining organizations. This paper presents an innovative Application Programmable Interface (API)-based hybrid model designed to enhance the detection performance of malware variants. This model integrates eXtreme Gradient Boosting (XGBoost) and an Artificial Neural Network (ANN) classifier, offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware authors. The model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features, providing a holistic… More >
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