An Improved Anomaly Detection in Wireless Sensor Network using Artificial Intelligence Evolving Optimization Tools

To maintain the security and dependability of wireless sensor networks (WSNs), anomaly detection is essential. In this research, we present a decision tree-based approach that is more effective for anomaly identification in WSNs. The goal is to increase the network's ability to detect anomalous behavior with greater accuracy and efficiency. To do this, we first gather and preprocess sensor data from several network nodes. Then, from the gathered data, pertinent features are identified, collecting traits that can distinguish between typical and abnormal behavior. These characteristics include time series analysis methods, frequency domain analysis, and statistical metrics. After that, a decision tree model is built using a training dataset that contains instances of normal and abnormal behaviour that have been labelled. Unseen data instances are fed through the decision tree model during the detection phase. Each occurrence is classified as normal or anomalous once it has gone through the decision tree once. Alerts and notifications can be produced by linking the detection system with the network management architecture, enabling network administrators to quickly address possible risks or problems. We demonstrate the efficiency of our methodology in increasing anomaly detection accuracy compared to existing methods through experimentation and evaluation. An effective and reliable method for spotting anomalies in WSNs is the decision tree algorithm. Additionally, decision trees are a useful tool for anomaly detection in wireless sensor networks due to their adaptability, which makes it simple to integrate with the network's changing features.