From Training to Edge Inference: A Transfer Learning Pipeline for Low-Latency Malware Classification on Snapdragon-Enabled Samsung Galaxy S23 Ultra (Android 13)

Onyedika Christopher Agada
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Onyedika Christopher Agada: Department of Cybersecurity, Uskudar University, Istanbul, Turkey.

Journal of Scientific Reports, 2025, vol. 11, issue 1, 51-64

Abstract: There is an urgent need for a lightweight detection system that is able to operate efficiently without dependency on the cloud, because of the large number of edge devices in interconnected systems. This study introduces a framework that deploys a deep learning-based malicious software classifier on resource-constrained edge devices, utilizing malicious software binary files that are visualized as images and fine-tuning a ResNet18 architecture to recognize visual patterns. The system was further optimized using the Qualcomm AI Hub for deployment on a Snapdragon-powered Samsung Galaxy S23 Ultra (Android 13). A test accuracy of 97.47% was achieved, with 0.978 precision, 0.978 recall, and an F1-score of 0.978. Additionally, a 75% model size reduction (44.8 – 11.2 MB) was achieved, and an on-device inference latency of 1 millisecond (batch size = 1) which is excellent for real-time malware detection. This research advances real-time threat mitigation in edge devices and enables scalable cyber defenses that preserve privacy.

Keywords: Deep Learning; Edge computing; Image Classification; Machine learning; Malware Detection; Transfer Learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aif:report:v:11:y:2025:i:1:p:51-64

DOI: 10.58970/JSR.1135

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