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This study explores the balance between security and energy consumption in Android malware detection by evaluating various Multi-Layer Perceptron (MLP) configurations. By comparing standard FP32 models with optimized INT8 quantized neural networks on the TUANDROMD and DREBIN datasets, the authors demonstrate that INT8 quantization can reduce model size by approximately 3.5 times and energy consumption to 0.0189 mJ per inference, while achieving over 99.2% detection accuracy. The findings indicate that shallow quantized architectures significantly enhance energy efficiency, paving the way for effective malware protection on resource-constrained devices.
INT8 quantization slashes energy consumption to 0.0189 mJ per inference without sacrificing malware detection accuracy, revolutionizing mobile security.
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 energy consumption to 0.0189 mJ per inference, while maintaining more than 99.2\% detection accuracy. We found that shallow quantized architectures, such as 3-layer and 4-layer QNNs, reduce energy costs by improving throughput and shortening the time of CPU operating in a high-power state. This work shows that efficient malware protection can be achieved on resource-constrained smartphones and provides a foundation for Green AI in mobile security.