Digital Twin-Driven Intrusion Detection for IoT and Cyber-Physical System

From smart homes to industrial automation, the efficiency and connectivity of many applications have been greatly improved by the fast expansion of the Internet of Things (IoT) and Cyber-Physical Systems (CPS). Sophisticated cyberattacks are becoming more common in this networked ecosystem, thus strong security measures are needed. In order to protect CPS and IoT settings, this study presents a new intrusion detection method that makes use of Digital Twin technology. To build an adaptable and dynamic Intrusion Detection System (IDS), our suggested architecture combines Digital Twins with state-of-the-art machine learning methods. In order to accurately detect and categorize any threats, the IDS constantly learns from data in both the past and the present. When tested on benchmark datasets and in real-world Internet of Things (IoT) settings, the framework outperforms conventional intrusion detection system (IDS) solutions in terms of detection accuracy and false alarm rate reduction. To protect IoT and CPS infrastructures from ever-changing cyber threats, this scalable and resilient security solution is offered by this IDS that is powered by digital twins. We present ICNN-FCID, an Integrated Convolutional Neural Network for Fog Computing Environment, a hybrid deep learning intrusion detection model for fog computing environments.