Enhanced Malware Family Classification via Image-Based Analysis Utilizing a Balance-Augmented VGG16 Model

The escalating sophistication and automation of malware generation techniques have led to an unprecedented proliferation of diverse and potent malicious software, thereby posing a considerable threat to individual, commercial, and digital security.Traditional detection systems often fall short in identifying these evolving threats, underscoring the critical necessity for advanced detection and classification strategies.Herein, we present an innovative deep learning approach for malware image analysis, employing a multi-layer VGG16 model-termed as MLVGGNET.We meticulously assess the performance of our proposed model using a representative dataset, embodying twenty-five distinct malware species, commonly known as the "Malimg" dataset.Our proposed model is evaluated with state-of-the-art techniques.The model's performance metrics include recall, specificity, accuracy, and the F1 score.Our investigations reveal that the MLVGGNET model, particularly when enhanced with class balancing techniques, demonstrates superior performance over existing methodologies.Remarkably, the incorporation of class balancing in the benign class results in highly promising outcomes.Despite its relative simplicity, our proposed MLVGGNET model exhibits robust efficacy in photograph intrusion detection systems, as substantiated by our empirical results.This study thus underscores the potential of our model as an efficient tool for the precise detection and classification of malware, outpacing current approaches.