---
title: "Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.20003v1"
permalink: "https://feed.astralor.com/items/itm_895bbe575b1dd843b0daab05"
published: "2026-07-22T10:39:42.000Z"
score: 74
channel: "tech"
tags: ["Android malware detection","arXiv","Green AI","INT8 quantization","MLP"]
---

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

## 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.

## At a glance

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.

## Key points

- 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

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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