Intrusion Detection in Wireless Sensor Networks Using Deep Learning

WSNs are currently seeing widespread adoption across a variety of settings, including the medical industry, manufacturing environments, and a number of other domains. WSNs are distinguished by a number of distinguishing qualities, including their limited power supply, use of bandwidth, and overall energy consumption. Because of this, traditional networks can be secured using a wide variety of methods, whereas wireless sensor networks (WSNs) cannot be protected using the same methods. In order to make wireless sensor networks (WSNs) safer as a whole, it was necessary to develop new ideas and approaches. The prevention of intrusion is the most important concern in wireless sensor networks (WSNs). This study develops a Deep Learning (DL) method for Intrusion Detection Systems (IDS) in Wireless Sensor Networks (WSNs) by making use of Dense Artificial Neural Networks (Deep-ANN). When compared to earlier models, the one that was developed using Ann achieved the highest level of accuracy, which was 96.45 percent. The ANN model outperforms the other ML models that are currently available thanks to its superior precise recall as well as F1 scores of 96.38, 98.94, and 97.64.