Two-Tier Anomaly Detection for an Internet of Things Network

Machine Learning (ML) has proven itself to be one of the most powerful tools to detect and prevent cyber-attacks. In this study, ML is used for anomaly detection in an Internet of Things (IoT) network. This paper proposes a Two-Tier Hybrid Learning Model for Clustering and Classification (TT-HLMCC) of unlabeled IoT data. The proposed method uses two algorithms for clustering: K-means and DBSCAN (Density Based Spatial Clustering for Applications with Noise). Random Forest is used for the final classification stage on the newly labeled data. The TT-HLMCC model (Proposed Model) is compared to the performance metrics of three other algorithms. While the TT-HLMCC model matches and slightly beats the performance of [3], our computational time is significantly lower.