Publication Date
10-1-2025
Document Type
Article
Publication Title
Electronics Switzerland
Volume
14
Issue
19
DOI
10.3390/electronics14193915
Abstract
This paper presents a comparative study on the impact of robust hashing in enhancing image-based malware classification. While Convolutional Neural Networks (CNNs) have shown promise when working with image-based malware samples, their performance degrades significantly when obfuscation techniques are taken into consideration to hamper the malware classification or detection. To address this, we apply a robust hashing technique that generates invariant visual representations of malware samples, enabling improved generalization under obfuscation implemented as image salting. Using a custom obfuscation method to simulate polymorphic variants, we evaluate MobileNet, ResNet, and DenseNet architectures across five salting conditions (0% to 40%). The results demonstrate that robust hashing substantially boosts classification accuracy, with DenseNet achieving 89.50% on unsalted data, compared to only 68.00% without hashing. Across all salting levels, models consistently performed better when robust hashing was applied, confirming its effectiveness in preserving structural features and mitigating adversarial noise. These findings position robust hashing as a powerful preprocessing strategy for resilient malware detection.
Keywords
adversarial attacks, Convolutional Neural Networks, cybersecurity, dataset obfuscation, DenseNet, malware detection, MobileNet, ResNet, robust hashing
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Computer Science
Recommended Citation
Sanket Shekhar Kulkarni and Fabio Di Troia. "Robust Hashing for Improved CNN Performance in Image-Based Malware Detection" Electronics Switzerland (2025). https://doi.org/10.3390/electronics14193915
