Deep Learning based Framework for Intelligent Threat Detection in IoT-Driven Cyber-Physical Systems

Abstract

The Internet of Things (IoT) technologies have been rapidly adopted in the field of Cyber-Physical Systems (CPS) and have greatly enhanced the automation, connectivity and operational efficiency of industrial and critical infrastructure (ICI) environments. The advent of greater device connectivity, however, has also grown the attack surface, leaving CPS environments open to many different cyber threats. The traditional methods of intrusion detection are not effective in detecting more complex and advanced attacks because the features are manually designed and cannot be easily adapted. In this publication, a Deep Learning (DL)framework for security monitoring is presented in this study, which uses CNN to improve the intrusion detection capability of the cyber-attack system in the IoT-enabled CPS environment. The intended framework accepts the network traffic data out of the UNSW-NB15 dataset to learn complicated cyber security threats and identify attacks from normal data traffic. We compared the proposed CNN model against a conventional Support Vector Machine (SVM) classifier. The experimental results demonstrate that the CNN model surpasses the others across key metrics, including precision, accuracy, F1 score and recall. The proposed approach has proved to be efficient in capturing hidden traffic characteristics and improves the reliability of cyber threat detection framework in dynamic IoT-based CPS systems. The evaluation results confirm that deep learning methods can be a scalable and efficient way to enhance the cybersecurity of next-generation cyber-physical infrastructures.