With the integration of smart devices and systems for increased convenience and efficiency, the Internet of Things (IoT) is revolutionizing many facets of human existence. However, this quick growth also makes IoT networks more vulnerable to cyberattacks, especially Denial of Service (DoS) attacks, which can seriously interfere with operations. Because of their high resource needs, traditional Intrusion Detection Systems (IDS) are not well-suited for Internet of Things scenarios. In view of the pressing requirement for strong security measures, this research focuses on creating a lightweight IDS, especially suited for IoT environments. The suggested system makes use of a Support Vector Machine (SVM)-based methodology, which is well known for being successful in classification tasks. This intrusion detection system’s originality is found in its simple architecture, which uses just two or three basic criteria to identify intrusions. This simplified method greatly lowers processing complexity, which makes it an energy-efficient solution perfect for Internet of Things devices with limited resources. The system’s excellent detection accuracy, despite its simplicity, guarantees accurate cyber threat identification without putting undue strain on the network. The effectiveness of the system is demonstrated by the simulation results, which show that it can detect intrusions with accuracy while using less energy and computing power. The suggested IDS is positioned as a viable option for improving the security of IoT networks due to its efficiency and accuracy balance. Through tackling the distinct obstacles of IoT security, this study advances the creation of more robust and secure IoT ecosystems, opening the door for the broader integration of IoT technology across several industries.