Malware Detection Based on Deep Learning
Leen Alsharafi, Maha Asiri, Sumayya Azzony, Ali Alqahtani · 2023
In this article, we delve into the realm of malware detection, We’ve created an advanced deep learning method designed to classify malicious software contained in an executable files. This stands in sharp contrast to traditional malware detection systems that depend on signatures, our method hinges on static detection techniques for discerning between malicious and benign files. We introduce an efficient deep learning technique for the identification of malware. Our proposed method leverages a 1D Convolutional Neural Network (1DCNN) specifically tailored to the characteristics of portable executable files. Empirically, we demonstrate the effectiveness of our approach by comparing it to state-of-the-art methods across three datasets. The experimental results unequivocally show that our approach surpasses several classification models, providing a practical, dependable, and swift means of detecting malware in executable files.