IoT Networking Security with Cloud Computing - A Comprehensive Examination of Threats, Vulnerability Analysis, Identification, Detection, and Mitigation Strategies with real time implementations

Abstract

With the widespread adoption of Internet of Things (IoT) devices across various industries, ensuring security has become a critical concern due to potential threats to user privacy and property. This article conducts a comprehensive review of IoT Networking Threats, focusing on Vulnerability Analysis, Identification, Detection, and Mitigation. The review categorizes the analysis, detection, and mitigation processes into five segments. It begins with an exploration of security protocols aimed at enhancing IoT network security by establishing identity and trust. Subsequently, it addresses vulnerabilities and attacks affecting IoT networks. The article then introduces an Intrusion Detection Mechanism utilizing Machine Learning (ML) and Deep Learning (DL) techniques for effective threat mitigation. Additionally, it explores new technologies employed in the threat mitigation process. The performance of these methods is evaluated using various metrics such as accuracy, error, precision, execution time, encryption time, and decryption time. The evaluation includes two scenarios: detection of anomalies in IoT networks and mitigation of threats in IoT networks. Notably, the APSO-CNN technique demonstrates superior accuracy, error, and precision values in attack detection, while ECC-CoAP exhibits efficient execution time in the mitigation process.