<![CDATA[The development of the Internet of Things (IoT) has brought convenience to variousaspects of life, but it also presents significant challenges regarding cybersecurity. One solutionto address this issue is the development of an Intrusion Detection System (IDS) based on machinelearning. This study aims to design an efficient and adaptive IDS for IoT environments usingmachine learning algorithms such as Random Forest and Support Vector Machine (SVM). Themethodology includes system design, data collection, algorithm selection, model training, andsystem performance evaluation. The results show that Random Forest and SVM algorithms areeffective in detecting attacks such as Distributed Denial of Service (DDoS) and malware, with arelatively high accuracy rate. However, the main challenges faced are the need for representativedatasets and computational efficiency issues on resource-constrained IoT devices. This studyconcludes that machine learning-based intrusion detection systems can improve IoT security byaccurately detecting cyber-attacks. Further development is expected to address efficiencyconstraints and enhance the system's reliability in facing increasingly complex threats.]]>
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