Strengthening IoT Intrusion Detection through the HOPNET Model
The rapid adoption of Internet-of-Things (IoT) applications has raised concerns about the security of IoT communication systems. The escalating number of harmful attacks poses severe threats, leading to network disruptions and system failures. In this research, we propose a novel solution called the Hyper-Parameter Optimized Progressive Neural Network (HOPNET) model, designed to effectively detect intrusions in IoT communication networks.To validate the effectiveness of the HOPNET model, We utilize the Nsl-Kdd dataset and perform extensive data preprocessing to rectify errors and extract features pertaining to various attack categories. The HOPNET model is executed on the Java platform, and we assess its performance through a comparative analysis with established intrusion detection methods. Our findings demonstrate that the HOPNET model outperforms state-of-the-art methods, exhibiting superior attack prediction scores and significantly reduced processing times. This underscores the significance of advanced intrusion detection methods in securing IoT communication systems.The key contributions of this research lie in the development of the HOPNET model, which provides a robust defense against evolving cyber threats, ensuring a safer and more reliable IoT ecosystem. As the IoT landscape continues to evolve, the HOPNET model sets a strong foundation for proactive security measures, offering a promising avenue for safeguarding IoT networks from malicious activities.