# Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection

arXiv: https://arxiv.org/abs/2607.20003 · HTML: https://mlsanity.com/paper/2607.20003/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 2 of 2)

- [Taming Energy Cost of Disk Encryption Software on Data-Intensive Mobile Devices](https://mlsanity.com/paper/1604.07543/) (2016)
- [ARENA: A tool for measuring and analysing the energy efficiency of Android apps](https://mlsanity.com/paper/2510.01754/) (2025)

## Abstract (arXiv)

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.