Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection
Score
74量化与能耗建模的研究已有，但针对移动端安全检测的完整实验对比有一定新意。
方法描述清晰，实验数据详实，结果可视化充分，逻辑严谨。
对智能手机厂商和移动安全开发者有直接的低功耗部署参考价值。
2025年7月预印本，紧跟绿色AI与边缘安全热点。
来自arXiv预印本，尚未同行评议，但依托公开数据集和标准方法。
Base 82 × T2 Source weight ×0.9 = 74
Curation line 70Selected
Why selected
Novel methodology with solid experimental results, providing clear guidance for resource-constrained scenarios.
AI commentary
「What makes this paper stand out is that it uses the simplest fully-connected network (MLP) plus quantization to resolve the seemingly contradictory trade-off between security and energy, showing that complex models are often unnecessary. For engineers deploying AI on mobile devices, the concrete energy data (0.0189 mJ per inference) and layer count recommendations are directly actionable.」
Quick overview
This study addresses the high energy consumption of deep learning models on Android devices by balancing malware detection performance and energy efficiency using INT8 quantization and shallow MLP architectures. Experiments show that INT8 quantization reduces model size by about 3.5 times and energy consumption to 0.0189 mJ per inference while maintaining over 99.2% detection accuracy. Three-layer and four-layer quantized neural networks further reduce energy costs by improving throughput and shortening CPU high-power state duration, providing a feasible pathway for green security detection on resource-constrained smartphones.
- INT8 quantization reduces model size by about 3.5x and energy consumption to 0.0189 mJ per inference
- Shallow quantized architectures (3-layer and 4-layer MLPs) significantly reduce energy while maintaining over 99.2% accuracy
- Energy savings achieved by improving throughput and shortening CPU high-power state duration
Full text
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