Enhancing Intrusion Detection in Wireless Sensor Networks Using Deep Convolutional Neural Networks

The rapidly growing number of Internet users has made security more and more crucial. Researchers have developed a number of intrusion detection systems that use data mining techniques to find intruders. Several articles offer datadriven techniques to safeguard the Wireless Sensor Network (WSN) toward such assaults. These techniques use either classic Machine Learning (ML) models. These approaches have disadvantages, such as relying on raw characteristics for intrusion detection, which lead to a low level of detection accuracy. This paper proposes an Intrusion Detection System (IDS) to prevent these problems that successfully detects intruders in wireless networks, ensuring secure data exchange by implementing an enhanced deep learning model to identify different sorts of assaults in the WSN. Deep convolutional Neural Network (DCNN) is recommended for avoiding different incursions. Proposed framework effectively addresses potential threats between the application's and access network layers. The proposed work implemented by Python software tool and performed efficiently obtain the results. The results of proposed system are compared to state of art. This study analyzes relevance of results and their future prognosis. This study introduces a proposed DCNN classifier that demonstrates an impressive accuracy of 97.3% and a sensitivity of 92.4%, as determined through comparative analysis of the datasets.