发表机构
MIE-SPPU Institute of Higher Education（MIE-SPPU高等教育学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究安卓恶意软件检测中安全与能源的矛盾，通过测试不同MLP模型配置，比较标准FP32与优化INT8量化神经网络，发现INT8量化能降能耗、缩模型大小且保持高准确率，浅量化架构可降成本，为移动安全绿色AI提供基础。
AI 中文摘要
先进安卓恶意软件的增加需要能在安卓设备上运行的深度学习模型。但安全与能源使用之间存在权衡，强大的检测模型会快速耗尽设备电池。这项工作测试了不同的多层感知器（MLP）模型配置，以平衡恶意软件检测性能和能源效率。使用TUANDROMD和DREBIN数据集，比较了标准FP32模型与不同模型深度的优化INT8量化神经网络的分类性能和能耗。结果表明，INT8量化使模型大小减少约3.5倍，每次推理能耗降至0.0189 mJ，同时保持超过99.2%的检测准确率。浅量化架构，如3层和4层QNNs，通过提高吞吐量和缩短CPU高功率状态下的运行时间来降低能源成本。这项工作表明可在资源受限的智能手机上实现高效的恶意软件保护，并为移动安全中的绿色人工智能奠定了基础。
英文摘要
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.
Comments6 pages, 1 figure
