One of the core ideas of Industry 4.0 has been the use of digital twin networks (DTNs). A DTN facilitates the co-evolution of real and virtual things through the use of DT modelling, interactions, computation, and information analysis systems. A DT simulates product lifecycles to forecast and optimize manufacturing systems and component behavior. Industry and Academia have been developing digital twin (DT) technology for real-time remote monitoring and control, transport risk assessment, and intelligent scheduling in the smart industry. This study aims to design and simulate a comprehensive digital twin model connecting three factories to a single server. It incorporates remote network control, IoT integration, advanced networking protocols, and security measures. The model utilizes the Open Shortest Path First (OSPF) routing protocol for seamless network connectivity within the interconnected factories. The Access Control List (ACL) and authentication, authorization, and accounting (AAA) mechanisms ensure secure access and prevent unauthorized entry. The digital twin model is simulated using Cisco Packet Tracer, validating its functionality in network connectivity, security, remote control, and motor efficiency monitoring. The results demonstrate the successful integration and operation of the model in smart industries. The networked factories exhibit improved operational efficiency, enhanced security, and proactive maintenance.

One of the biggest cybersecurity challenges in recent years has been the risk that insiders pose. Internet consumers are susceptible to exploitation due to the exponential growth of network usage. Malware attacks are a major concern in the digital world. The potential occurrence of this threat necessitates specialized detection techniques and equipment, including the capacity to facilitate the precise and rapid detection of an insider threat. In this research, we propose a machine learning algorithm using a neural network to enhance malware detection accuracy in response to insider threats. A feature extraction, anomaly detection, and classification workflow are also proposed. We use the CERT4.2 dataset and preprocess the data by encoding text strings and differentiating threat and non-threat records. Our developed machine learning model incorporates numerous dense layers, ReLU activation functions, and dropout layers for regularization. The model attempts to detect and classify internal threats in the dataset with precision. We employed random forest, naive Bayes, KNN, SVM, decision tree, logical regression, and the gradient boosting algorithm to compare our proposed model with other classification techniques. Based on the results of the experiments, the proposed method functions properly and can detect malware more effectively and with 100% accuracy.