Securing WSN-IoT Networks using SwinAlert-GAN: A Deep Learning-based Intrusion Detection Framework

The installation of intrusion detection systems represents an absolute necessity for Wireless Sensor Networks (WSN) together with Internet of Things (IoT) systems because such networks steadily integrate in crucial applications covering healthcare facilities as well as smart cities and industrial automation. IoT devices remain open to diverse security risks and cyberattacks because they process sensitive information while maintaining strong network connectivity. Normal security protocols operate inadequately when dealing with complex data volumes from these networks so professionals need to build efficient intrusion detection systems (IDS) to protect against malicious threats. The main barrier to designing successful IDS systems for WSN and IoT networks involves attaining precise detection and few false alerts while monitoring complex real-time attacks. This paper introduces SwinAlert-GAN as a new model design targeted at detecting and classifying WSN-IoT network intrusions. The system gains its novelty through a hybridized framework which joins the Swin Transformer’s attention-efficient design with GAN-generated data to produce a highly versatile and efficient intrusion detection system. The presented model achieved superior results using NSL-KDD and BoT-IoT along with WSN-DS benchmark datasets where it produced higher accuracy and precision together with better recall and F1-score than existing state-of-the-art detection techniques.