A Review of IoT Networking Threats and Vulnerability Analysis, Identification, Detection, and Mitigation in Communication Systems

Abstract

The proliferation of Internet of Things (IoT) devices has been remarkable, driven by the ever-expanding landscape of IoT applications. These devices find widespread use across diverse industries, spanning from smart homes, smart apparel, and smart manufacturing to smart cars and smart medical care. Amid this wide array of applications, security stands out as a paramount concern, given the potential risks it poses to user privacy and property. In response to these challenges, numerous scholars and innovators have been actively developing applications and solutions aimed at mitigating the threats that loom over IoT networks. These endeavors seek to strike a delicate balance between safeguarding IoT systems and selecting the most effective measures to prevent and combat potential attacks. This article, therefore, embarks on a comprehensive review of IoT Networking Threats and Vulnerability Analysis, covering aspects of identification, detection, and mitigation. The review is structured into five categories to provide a comprehensive analysis. It begins by delving into security protocols designed to fortify IoT networks through the establishment of robust identity and trust mechanisms. Subsequently, the study scrutinizes the various vulnerabilities and attacks that pose threats to IoT networks. In the quest for enhanced security, the article explores the use of Intrusion Detection Mechanisms, harnessing the power of Machine Learning (ML) and Deep Learning (DL) techniques. These mechanisms serve as a shield against impending threats. Moreover, the article delves into the adoption of new technologies to bolster the thread mitigation process. To gauge the effectiveness of these methods, the performance evaluation encompasses an array of metrics, including accuracy, error rates, precision, execution time, encryption time, and decryption time. Two critical scenarios are considered in this evaluation: the detection of anomalies within IoT networks and the mitigation of threats within these networks. Among the various techniques explored, APSO-CNN emerges as a standout performer, exhibiting superior accuracy, minimal error rates, and high precision in the detection of attacks. Furthermore, ECC-CoAP is identified as the most efficient mitigation strategy, particularly excelling in execution time. In conclusion, this review endeavors to shed light on the multifaceted realm of IoT network security by addressing threats and vulnerabilities, harnessing advanced technologies, and assessing the performance of these methods. It serves as a valuable resource for the ongoing efforts to fortify IoT systems against potential risks and attacks.