A Three-Layered Security and Gap Analysis Approach for IoT Cybersecurity with Machine Learning-Based Attack Detection

The rapid advancement in IoT devices has proposed significant cybersecurity risks across multiple layers, including the IoT layer, cloud computing layer, and big data processing layer. This paper presents a comprehensive threelayered security and gap analysis framework aimed at mitigating IoT security risks. The proposed framework identifies threats in each layer, examines security gaps, and employs machine learning techniques for cyberattack detection. Through mathematical modelling, algorithmic solutions, and empirical analysis, we demonstrate the effectiveness of our approach in detecting and mitigating security threats. The results show improved anomaly detection rates, reduced false positives, and enhanced overall system resilience. The contributions of this study include a novel multi-layered security framework, an adaptive threat detection algorithm, and an indepth security gap analysis across the IoT ecosystem.