Multi-View Feature Fusion for Effective Malware Classification Using Deep Learning

The rapid increase in global malware infections has necessitated the development of robust malware detection systems to mitigate threats, such as ransomware and crypto-miners, that aim for financial gain. Deep learning-based Convolutional Neural Network (CNN) model for classifying malware in Portable Executable (PE) binary files using a fusion feature set approach. An extensive evaluation of various deep learning architectures and machine learning classifiers, including Support Vector Machines (SVM), was conducted across multi-aspect feature sets encompassing static, dynamic, and image-based features, ultimately selecting the CNN model for optimal performance. The model achieved an accuracy using fusion feature sets, demonstrating its robustness and generalizability on unseen malware datasets