ANOMALY DETECTION IN NEXT-GEN IOT:GIANT TREVALLY OPTIMIZED LIGHTWEIGHT FORTIFIED ATTENTIONAL CONVOLUTIONAL NETWORK

Within the most recent version of security monitoring solutions crafted for interconnected device networks faces limitations due to data scarcity, diverse device types, and limited computational resources. Unlike traditional solutions, these networks require a different approach. To address these limitations, the paper introduces LF-ACANet-GTOA, a novel approach leveraging a unique architecture called Lightweight Fortified Attentional Convolutional Network. This model is optimized with the Giant Trevally Optimization Algorithm (GTOA) for efficient and accurate intrusion detection within resource constrained IoT networks. The system focuses on critical information within network traffic data using an attention mechanism. It analyses two public datasets such as CIC-IDS-2017 and Bot-IoT to assess the effectiveness of LF-ACANet-GTOA. A meticulous pre-processing stage ensures clean and consistent data for the model. It provides a detailed description of the LF-ACANet-GTOA design, encompassing its components: Convolutional Encoder, Feature Enrichment Block, Attention Mechanism Integration, and Classification Layer. Additionally, it utilizes the Giant Trevally Optimization Algorithm (GTOA) for efficient training and optimization. The simulation results for the proposed LF-ACANet-GTOA method on the CIC-IDS-2017 dataset are promising, achieving high accuracy (99.57%), precision (99.26%), recall (99.16%), and F-score (99.21%), with low false alarm (0.73%) and miss rates (0.83%). These results suggest that LF-ACANet-GTOA has the potential to be a robust and secure solution for intrusion detection in resource-constrained interconnected device networks.