Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection
An increase in advanced Android malware requires the use of deep learning models, which can run on Android devices. But there is a trade-off between security and energy use, as strong detection models can drain the battery of devices fast. This work tests different Multi-Layer Perceptron (MLP) model configurations to balance malware detection performance and energy efficiency. In this work, we compared standard FP32 models with optimized INT8 quantized neural networks with different model depths using TUANDROMD and DREBIN datasets for both classification performance and energy consumption. The results show that INT8 quantization reduces model size by about 3.5 times with a decrease in ene...
| Authors | Shrinidhi Sridhar, Vikas K. Malviya |
|---|---|
| Primary category | cs.CR |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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