Anomaly detection using ensemble random forest in wireless sensor network

In the field of wireless sensor network (WSN), anomaly detection is an important task. In this work, we have presented an anomaly detection process using ensemble random forest (ERF) in wireless sensor networks. We choose Decision Tree, Naive Bayes, and K-Nearest Neighbor as the base learners of the ensemble. We also used bootstrap sampling to construct the random forest. Here, we used python 3.7.7 with machine learning module sci-kit learn 0.23.1 to implement our learning algorithm. We evaluated our ERF algorithm using a real-world sensor dataset, namely activity recognition based on multi-sensor data fusion (AReM) dataset. We used performance metrics, namely, accuracy, sensitivity, specificity, precision, recall, f measure, and Gmean, to show that our novel ERF performs better than the base learners in isolation. We also showed the misclassification error for out-of-bag data. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.