As Internet of Things (IoT) grow rapidly, security concerns have escalated due to the massive interconnection of industrial devices. Traditional static knowledge graphs fall short in representing evolving threats and real-time security events. To address this limitation, Temporal Knowledge Graphs (TKGs) have been proposed, enabling the modeling of time-dependent relations and entities that reflect dynamic security incidents. However, issues such as sparse threat intelligence and incomplete event data remain, necessitating the development of Temporal Knowledge Graph Completion (TKGC) techniques to predict potential threats and update security knowledge bases in real time. In this review, we provide a comprehensive overview of TKGC approaches, categorizing them into interpolation-based and extrapolation-based methods. We analyze recent advancements in the field, emphasizing their technological improvements over earlier techniques. Furthermore, we explore the distinct challenges presented by IoT and industrial security contexts and outline promising future directions to enhance the applicability and effectiveness of TKGC in safeguarding connected systems.