用量化模型在省电前提下实现高精度安卓恶意软件检测
Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection
- 采用INT8量化降低模型体积与能耗
- 每推理仅耗0.0189毫焦，准确率超99.2%
- 浅层量化网络提升吞吐量，适合手机端部署
随着高级安卓恶意软件增多，深度学习模型被用于设备端检测，但其安全性能与能耗存在矛盾。本文测试不同多层感知机（MLP）配置，在TUANDROMD和DREBIN数据集上对比标准FP32模型与优化后的INT8量化神经网络，评估分类性能与能耗。结果表明，INT8量化使模型尺寸缩小约3.5倍，单次推理能耗降至0.0189毫焦，同时保持超过99.2%的检测准确率。研究发现，3层和4层浅层量化网络通过提升吞吐量并缩短CPU高功耗运行时间，显著降低能耗。该工作证明了在资源受限手机上实现高效恶意软件防护的可行性，为移动安全中的绿色人工智能奠定基础。
原文摘要 · Abstract (English)
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.
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