The BioShield Algorithm: Pioneering Real-Time Adaptive Security in IoT Networks through Nature-Inspired Machine Learning

This paper introduces the BioShield Algorithm, aimed at the crucial task of securing IoT networks through real-time adaptive mechanisms that draw inspiration from nature. It delves into the critical issues plaguing IoT security, such as the dynamic and heterogeneous nature of both threats and network architectures. It proposes a nature-inspired machine learning model designed for adaptive, real-time threat detection and mitigation. By employing the "UNSW-NB15" dataset, the algorithm undergoes a rigorous evaluation across various metrics, including detection accuracy, response time, and scalability. The quantitative analysis reveals the algorithm's high proficiency in dealing with diverse cyber-attack scenarios, with precision scores ranging from 95.9% for Malware to 98.4% for Tampering attacks. Recall rates also show impressive figures, peaking at 96% for DDoS attacks, alongside consistently high F1 scores that underscore the model's balanced precision and recall capabilities. Additionally, accuracy rates across different attack types further confirm the algorithm's effectiveness, with scores oscillating between 94.95% and 97.2%. These results strongly endorse the BioShield Algorithm's capacity to accurately detect and classify cyber threats within IoT environments, spotlighting its applicability in significantly enhancing the security framework of IoT networks. This algorithm stands out for its adaptive, efficient, and scalable nature, positioning it as a pivotal contribution to the field of IoT security.