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Judul	Sitasi	Tahun
IoT, Anomaly Detection, Machine Learning, K-Nearest Neighbors, Random Forest, Real-Time Detection (James Anderson, Emily Johnson, Michael Brown) DOI : 10.62951/ijies.v1i1.50 - Volume: 1, Issue: 1, Sitasi : 0 29-Feb-2024 \| Abstrak \| PDF File \| Resource \| Last.06-Aug-2025 Abstrak: The increase in connected IoT devices causes increased vulnerability to cyber attacks. This research develops a hybrid machine learning model to detect real-time anomalies in IoT networks. This model combines the K-Nearest Neighbors (KNN) and Random Forest (RF) algorithms to increase accuracy and efficiency. Evaluation was carried out using the UNSW-NB15 dataset to test model performance. The results show that this hybrid approach is able to detect anomalies with high accuracy and a low false positive rate.	0	2024