Distributed and Lightweight Intrusion Detection for IoT

Abstract

The internet of things (IoT) represents a burgeoning paradigm extensively employed in crafting intricate real-time applications, fostering interconnectedness and convenience. IoT applications suffer from several cybersecurity vulnerabilities. In pursuit of this objective, an IDS model called lightweight pyramidal U-Net with dual inception fusion framework is proposed. The IoT device layer comprises devices that continually generate network traffic for cloud applications which are directed to the edge layer, where two operations occur: traffic filtering and pre-processing and feature extraction. The model incorporates feature attention in the encoder phase and multiscale pyramidal layers in the decoder phase to robustly extract network features by capturing interdependencies among them. Experimental results demonstrate the superiority of our approach over state-of-the-art methodologies.