Work

An Energy-Efficient Intrusion Detection Offloading Based on DNN for Edge Computing

Year: 2025

Type: article

Abstract: To address the computational limitations associated with implementing Deep Neural Network (DNN)–based intrusion detection on resource-constrained devices, this work proposes an energy-efficient edge architecture that integrates distributed early-exit DNN models to minimize processing overhead while preserving detection performance. Our approach employs multi-objective optimization to dynamically offload complex tasks to the cloud, thereby balancing the trade-off between accuracy and energy consumption under operator constraints. Furthermore, it incorporates a rejection mechanism and confidence calibration via temperature scaling to ensure reliability as network traffic evolves. Experiments on a 7TB year-long dataset demonstrate that the system reduces edge energy consumption to only 1% while offloading only 10% of events, all without compromising detection accuracy and even improving the F1-Score by 0.02 compared to traditional approaches.

Source: IEEE Internet of Things Journal

DOI: 10.1109/jiot.2025.3544060

Authors: João A. Simioni, Eduardo K. Viegas, Altair O. Santin, Everton de Matos

Institutions: Pontifícia Universidade Católica do Paraná, Technology Innovation Institute

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,645