The Social Internet of Things (SIoT) facilitates seamless interactions between IoT devices, providing users with quick and convenient services. However, this domain is vulnerable to manipulation by malicious nodes that issue false recommendations and services to inflate their reputation, leading to trust-related attacks. Developing trust models to detect these attacks in each interaction is challenging due to the complexity of the patterns and features required for accurate prediction. Furthermore, trust metrics are not consistently updated for each node, resulting in inefficiencies and unnecessary resource consumption. To address these challenges, we propose a system that analyzes the context of the current interaction and incorporates temporal factors to monitor node behavior. Our approach employs a decentralized system based on blockchain and IPFS storage, reducing costs and making the process of trust evaluation more efficient and practical for real-time scenarios. This method enhances the detection of trust-related attacks while optimizing resource allocation and execution time.