MAD-ANET: Malware Detection Using Attention-Based Deep Neural Networks

Abstract

In the current digital era, new technologies are becoming an essential part of our lives. Consequently, the number of malicious software or malware attacks is rapidly growing. There is no doubt, the majority of malware attacks can be detected by most antivirus programs. However, such types of antivirus programs are one step behind malicious software. Due to these dilemmas, deep learning become popular in the detection and classification of malicious data. Therefore, researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and models. In this research, we presented a lightweight attention-based novel deep Convolutional Neural Network (DNN-CNN) model for binary and multi-class malware classification, including benign, trojan horse, ransomware, and spyware. We applied the Principal Component Analysis (PCA) technique for feature extraction for binary classification. We used the Synthetic Minority Oversampling Technique (SMOTE) to handle the imbalanced data during multi-class classification. Our proposed attention-based malware detection model is trained on the benchmark malware memory dataset named CIC-MalMem-2022. The results indicate that our model obtained high accuracy for binary and multi-class classification, 99.5% and 97.9%, respectively.