<![CDATA[Internet of Things (IoT) is a technology that allows various physical devices to connect to each other and exchange data via the internet network. The application of IoT is increasingly widespread in various sectors such as smart homes, manufacturing industries, smart agriculture, and healthcare. However, along with the increasing number of devices and the volume of data traffic sent, the potential risk of cybersecurity threats is also increasing. The large number of IoT devices that have limited computing capabilities makes the system more vulnerable to various attacks, including intrusion, exploitation of system weaknesses, and Distributed Denial of Service (DDoS) attacks. Therefore, early detection of anomalies in network traffic is a crucial aspect to maintain the security and stability of IoT systems. This study aims to develop and implement a Support Vector Machine (SVM)-based architecture as a classification method in an anomaly detection system on an IoT network. SVM was chosen because of its ability to handle high-dimensional data and non-linear classification effectively. The methodology used includes the process of extracting features from IoT network traffic datasets, data normalization, model training using the SVM algorithm, and evaluating model performance in distinguishing between normal and anomalous traffic. Thus, the implementation of SVM architecture can be an effective and efficient solution in intrusion detection systems for IoT networks. This research also opens up opportunities for the development of more adaptive security systems by integrating machine learning-based detection models into large-scale IoT infrastructures.]]>
