Enhanced IoT threat detection using Graph-Regularized neural networks optimized by Sea-Lion algorithm

The Internet of Things (IoT) has revolutionized business operations, but its interconnected nature introduces significant cyber security risks, including malware and software piracy that compromise sensitive data and organizational reputation. To address this challenge, we propose IoT Threat Detection using Graph-Regularized Neural Networks Method. Our framework utilizes the Google Code Jam Dataset, which is pre-processed using Edge-Aware Smoothing Sharpening Filtering (EASSF) to enhance data quality, and feature extraction is performed using the General Synchro extracting Chirplet Transform (GSCT). The AGRNN classifier is then employed to distinguish between benign and malicious threats, with optimization performed using the Sea-lion Optimization Algorithm. Our approach demonstrates exceptional performance, achieving accuracy improvements of up to 29.60% over existing methods. Specifically, the proposed work achieves 29.60%, 18%, and 14.7% higher accuracy for detecting benign threats, and 20.1%, 27.6%, and 13.2% higher accuracy for malicious threats compared to existing methods. Furthermore, our approach achieves 17.9%, 26.1%, and 13% higher F-measure for benign threats, and 16.7%, 35.6%, and 17% higher F-measure for malicious threats. The ROC analysis also confirms the effectiveness of our approach, with 29.02%, 18.0%, and 14.7% higher ROC values compared to existing methods. These results confirm the effectiveness of our approach in detecting cyber security threats in IoT systems, providing a robust solution for safeguarding sensitive data and protecting organizational reputation. By enhancing IoT security, our proposed work offers a promising approach for organizations seeking to bolster their cyber security defences.