Lightweight Autoencoder-Isolation Forest Anomaly Detection for Green IoT Edge Gateways

2025-11-22

AI Summary

Existing anomaly detection schemes for IoT edge gateways prioritize accuracy over energy efficiency, failing to balance detection performance with sustainability. To address this, we propose EcoDefender, a lightweight, green anomaly detection framework. EcoDefender innovatively integrates autoencoder (AE)-based representation learning with isolation forest (iForest)-based anomaly scoring, forming a hybrid architecture backed by theoretical guarantees. It introduces the first coupled analytical model jointly characterizing stability, convergence, and energy-complexity, explicitly embedding sustainability metrics into the security evaluation framework. Evaluated on real-world IoT traffic, EcoDefender achieves 94% detection accuracy, consumes only 22% average CPU utilization, and incurs merely 27 ms inference latency—reducing energy consumption by 30% compared to pure AE-based approaches. The framework significantly enhances the co-optimization of security and energy efficiency on resource-constrained edge devices.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The rapid growth of the Internet of Things (IoT) has given rise to highly diverse and interconnected ecosystems that are increasingly susceptible to sophisticated cyber threats. Conventional anomaly detection schemes often prioritize accuracy while overlooking computational efficiency and environmental impact, which limits their deployment in resource-constrained edge environments. This paper presents extit{EcoDefender}, a sustainable hybrid anomaly detection framework that integrates extit{Autoencoder(AE)}-based representation learning with extit{Isolation Forest(IF)} anomaly scoring. Beyond empirical performance, EcoDefender is supported by a theoretical foundation that establishes formal guarantees for its stability, convergence, robustness, and energy-complexity coupling-thereby linking computational behavior to energy efficiency. Furthermore, experiments on realistic IoT traffic confirm these theoretical insights, achieving up to 94% detection accuracy with an average CPU usage of only 22%, 27 ms inference latency, and 30% lower energy consumption compared to AE-only baselines. By embedding sustainability metrics directly into the security evaluation process, this work demonstrates that reliable anomaly detection and environmental responsibility can coexist within next-generation green IoT infrastructures, aligning with the United Nations Sustainable Development Goals (SDG 9: resilient infrastructure, SDG 13: climate action).

Problem

• Detecting cyber threats in resource-constrained IoT edge environments
• Balancing detection accuracy with computational efficiency and energy consumption
• Integrating sustainability metrics into anomaly detection for green IoT infrastructure

Innovation

• Integrates Autoencoder with Isolation Forest for detection
• Provides theoretical guarantees for stability and efficiency
• Achieves high accuracy with reduced energy consumption