Abstract
Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover new ways to evade these defense techniques. Traditional static and dynamic analysis methods are ineffective in identifying new malware and pose high overhead in terms of memory and time. Typical machine learning approaches that train a classifier based on handcrafted features are also not sufficiently potent against these evasive techniques and require more efforts due to feature-engineering. Recent malware detectors indicate performance degradation due to class imbalance in malware datasets. To resolve these challenges, this work adopts a visualization-based method, where malware binaries are depicted as two-dimensional images and classified by a deep learning model. We propose an efficient malware detection system based on deep learning. The system uses a reweighted class-balanced loss function in the final classification layer of the DenseNet model to achieve significant performance improvements in classifying malware by handling imbalanced data issues. Comprehensive experiments performed on four benchmark malware datasets show that the proposed approach can detect new malware samples with higher accuracy (98.23% for the Malimg dataset, 98.46% for the BIG 2015 dataset, 98.21% for the MaleVis dataset, and 89.48% for the unseen Malicia dataset) and reduced false-positive rates when compared with conventional malware mitigation techniques while maintaining low computational time. The proposed malware detection solution is also reliable and effective against obfuscation attacks.
Highlights
The proposed model was evaluated with four malware datasets: Malimg [10], Microsoft’s BIG 2015 [11], MaleVis [12], and Malicia [16]
The results indicate that the proposed DenseNet-based malware detection model takes less time to train and test the samples when compared to other deep learning-based malware detection systems
We proposed an efficient malware detection and classification technique that combines malware visualization and a pretrained DenseNet model with a reweighted categorical cross-entropy loss criterion
Summary
Very thorough understanding of the malware and its operations Though these methods are employed, combating new malware efficiently is becoming requires memory as well as time. Entropy 2021, 23, 344 by malware writers are recognizable in new variants, but the overall structure of the image remains unaffected Since it is crucial in detecting malware and to avoid information loss, no other approach for visualization is effective. A novel method is presented to classify malware variants based on the deep learning DenseNet model [14] enhanced with a class-balanced loss for reweighting the categorical cross-entropy loss. An effective and expeditious deep learning-based malware detection and classification system using raw binary images while requiring no binary execution (behavioral analysis), reverse engineering, or code disassembly language skills is provided.
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