Intrusion Detection Systems (IDS) plays a major role in modern network security strategies, offering various methods and architectures to analyze network access. These systems can be roughly labelled as two categories: signature-based and anomaly-based. Signature - based IDS monitor events using a database of known intrusions, while passive IDS focus on understanding system behavior and identifying anomalies. However, with the rapid development of the IoT, new and complex security challenges possess emerged. Despite efforts to address IoT cybersecurity through various technologies, further development is essential to effectively safeguard IoT ecosystems. A well-known approach to enhance IoT security involves integrating machine learning techniques. Numerous studies have explored the application of deep learning and machine learning methods to improve Internet of Things security. This research study has developed a deep learning based method to detect attacks on IoT systems. By employing Python programming and tools such as Tensorflow, Scikit-learn, and Seaborn, the efficiency of deep learning models is utilized in enhancing detection accuracy. The resultant findings suggest that deep learning holds significant promise for enhancing IoT security measures, providing a more robust defense against cyber threats targeting IoT devices and networks. This research study has contributed to enhancing the arena of IoT security, addressing a critical need in the constantly changing field of cybersecurity.