Enhancing real-time intrusion detection and secure key distribution using multi-model machine learning approach for mitigating confidentiality threats

Ensuring strong security measures against intrusions is of utmost importance in the ever-changing field of information management systems. Conventional Intrusion Detection Systems (IDS) frequently have difficulties in dealing with the ever-changing and intricate characteristics of contemporary cyber threats, particularly in the realm of the Internet of Things (IoT). The current body of research emphasizes the difficulties in attaining both high precision and real-time speed while still preserving the anonymity of data. This work tackles these concerns by presenting a scalable multi-model Machine Learning (ML) technique developed to improve real-time intrusion detection and ensure safe cryptographic key distribution. The suggested solution takes use of the widespread use of IoT devices, which increases the likelihood of advanced cyberattacks. Our approach involves implementing a ML-based automated IDS specifically designed for various IoT environments. These IDS enhance adaptability and accuracy. We also utilize Maximum–Minimum (Max–Min) normalization on the UNSW-NB15 and CICIoT2023 datasets to improve the accuracy of detecting intrusions. Furthermore, we classify a wide range of contemporary threats and typical internet traffic into nine distinct attack categories. To streamline data processing and improve system efficiency, we employ Principal Component Analysis (PCA) for dimensionality reduction. Additionally, we deploy six advanced ML models to optimize detection capabilities and accurately identify threats. Finally, we develop a secure key distribution mechanism using synchronized Artificial Neural Networks (ANNs). The process of mutual learning guarantees the secure distribution of keys among IoT networks, thus reducing the risks to secrecy. This novel methodology not only reinforces the ability to identify intrusions in real-time, but also improves the overall security stance of information management systems. This work significantly contributes to the field of digital security in information management by addressing the limits of current IDS solutions and presenting a complete, multi-faceted security strategy.