IoT, Anomaly Detection, Machine Learning, K-Nearest Neighbors, Random Forest, Real-Time Detection

James Anderson
Emily Johnson
Michael Brown

International Journal of Information Engineering and Science
Vol. 1 No. 1 (2024): February : International Journal of Information Engineering and Science

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.