Wireless Sensor Networks (WSNs) are prone to different security threats because of their open communication environment, distributed architecture, and resource constraints. For the security and integrity of a network to be ensured, robust intrusion detection systems (IDS) are required. The WSN-DS dataset has been used to provide an effective machine learning (ML) and Deep Learning (DL) based IDS and attack detection technique for WSNs. Several learning models, including K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Convolutional Neural Networks (CNN), Support Vector Machine (SVM), Logistic Regression (LR), and Neural Networks (NN), are compared in terms of performance. P... More >