Intrusion Detection in IoT Systems Using Denoising Autoencoder

Protection against unwanted intrusions is crucial for preserving the integrity and security of connected devices in the context of Internet of Things (IoT) networks. The growing number of IoT devices has made several industries more vulnerable to cyberattacks and security breaches, including smart homes, industrial automation, and healthcare. In response to this pressing dilemma, the goal of this project is to create a novel method for intrusion detection in Internet of Things systems utilizing Denoising Autoencoder (DAE) models. Traditional intrusion detection methods often prove inadequate in Internet of Things scenarios due to resource restrictions, dynamic network topologies, and a diversity of communication protocols. By utilizing DAEs’ unsupervised learning and feature extraction skills, our suggested approach creates a system that can identify and stop intrusion attempts in real-time. The evaluation of the study additionally makes use of the NSL-KDD and CICIDS 2017 datasets. DAE integration yields an unequaled accuracy of 99.991% when the CICIDS 2017 dataset is used, and an accuracy of 99.4% when the NSL-KDD dataset is used. The CICIDS 2017 dataset analysis reveals several notable performance measures, including an accuracy of 1.0, a precision of 0.995, and an F1-score of 0.998. Analyses of the NSL-KDD dataset also produce outstanding results, with an F1-score of 0.989, recall of 0.991, accuracy of 0.994, and precision of 0.984. The results also show how well the suggested DAE-based intrusion detection method works to stop unauthorized users from accessing IoT devices, which lowers the risk of issues with system integrity, privacy, and security. By strengthening resilience against evolving cyber threats in the networked Internet of Things landscape, this research enhances cybersecurity strategies tailored to address the unique challenges encountered by IoT ecosystems.