An Energy-Efficient Intrusion Detection Offloading Based on DNN for Edge Computing - Simioni, João A.; Viegas, Eduardo K.; Santin, Altair O. (Anais do Concurso de Teses e Dissertações da SBC (CTD-SBC); 2026: Anais do XXXIX Concurso de Teses e DissertaçõesDO - 10.5753/ctd.2026.19705, 2026)
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.