Enhancing Image-Based Malware Classification: A Feature Fusion Approach With Dwarf Mongoose Optimization Algorithm
DOI:
https://doi.org/10.29284/sjy8b550Keywords:
Malware Detection, Feature Fusion, Hyperparameter Tuning, Deep Belief Network, CapsNet, SimAM-EfficientNet.Abstract
The rapid advancement in networks and communication technologies has significantly increased the complexity of systems, resulting in a surge of sophisticated malware designed to evade detection. Traditional methods of manually examining malware are time-consuming and often ineffective. In response to these challenges, this research introduces an automated image-based malware classification technique using an Optimal Deep Learning-Based Feature Fusion Approach (AIBMC-ODLFFA). The primary goal of the AIBMC-ODLFFA technique is to accurately identify and classify malware by utilizing a feature fusion method that integrates two deep learning models: Capsule Network (CapsNet) and SimAM-Efficient Net. These models extract comprehensive features from malware images, which are then classified using a Deep Belief Network (DBN). The performance of the DBN is further enhanced through hyperparameter tuning using the Dwarf Mongoose Optimization Algorithm (DMOA). Experimental evaluations conducted on the Malimg malware dataset demonstrate that the AIBMC-ODLFFA approach significantly outperforms existing methods in terms of accuracy and computational efficiency. The results indicate that this technique holds great potential for practical application in automated and precise malware detection systems.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
