<![CDATA[The convergence of the Internet of Things (IoT) and Society 5.0 has successfully led to a human-centered and data-driven life ecosystem. IoT has become the backbone for infrastructure implemented in various domains, ranging from smart homes and smart farming to smart industrial environments. Nevertheless, as IoT devices become more connected and integrated into the ecosystem, the attack surface expands and network security becomes more challenging. The massive convergence and connectivity of IoT devices have a high potential for attacks on network infrastructure, such as Denial of Service (DoS), port scanning, exfiltration, brute force, and man-in-the-middle attacks. This study aims to detect anomalies in IoT network traffic by applying the Isolation Forest (IF) algorithm. The dataset was obtained from an IoT gateway connected to smart home devices and includes features such as data packet size, connection duration, source and destination capacity, attack protocols used, and the connection status of each device. The experimental results of this study indicate that the IF method can identify smart home device attacks with a competitive level of accuracy. The results of the anomaly analysis are then presented through a confusion matrix, classification report, and analytical visualizations such as 2D PCA, t-SNE, heatmap, and temporal distribution of anomalies. This study declares that the IF method contributes effectively to the analysis of Intrusion Detection Systems (IDS) in IoT environments such as smart homes that are heterogeneous and dynamic]]>
Copyrights © 2026
