IoT (Internet of Things) networks are growing at a rapid pace, and their dynamic and diverse nature has created enormous security challenges. With their high false positive rates and restricted ability to adapt to new threats, traditional intrusion detection systems (IDS) frequently fail to provide adequate protection for Internet of Things environments. By amalgamation anomaly detection techniques and trust management, this paper offers a novel trust-based intrusion detection system (IDS) framework that improves Internet of Things security. An intrusion detection engine, a trust-based decision-making module, and trust management make up the three main parts of the suggested system. Every IoT device has its trust score continuously evaluated and updated by trust management based on its historical interactions and behavior. The Isolation Forest algorithm is used by the intrusion detection engine to detect deviations from typical behavior patterns, which allows it to identify unusual activity. Using trust scores, the trust-based decision-making module prioritizes alerts from low-trust devices and lessens the scrutiny on high-trust devices, thereby improving the anomaly detection process. By using this method, false positives are reduced and detection accuracy is increased. The trust-based intrusion detection system performs better than conventional techniques when tested on synthetic data, exhibiting notably lower false positive rates and improved detection accuracy. These results demonstrate how trust-based models can improve IoT security by providing a flexible and scalable solution. This paper addresses the shortcomings of conventional systems in dynamic IoT environments and advances the development of resilient IDS frameworks.