Intrusion Detection of Local Area Network Using Digital Twin Technology
No Thumbnail Available
Date
2024
Authors
Amusan, A.A
Amusan, E.A
Adeniyi, E.O
Journal Title
Journal ISSN
Volume Title
Publisher
Editura Universitatii din Pitesti
Abstract
The rapid growth of Local Area Networks (LANs) in critical infrastructure necessitates robust security measures to mitigate threats such as intrusions. This paper presents a novel approach to LAN intrusion detection using digital twin technology integrated with a variety of tools, including the Fiware Orion Context Broker, Grafana IoT Agent, Firebase, and Flutter. The digital twin replicates the physical LAN environment in real-time, facilitating enhanced monitoring and real-time threat detection. The system uses Snort 3 as a network intrusion detection system (NIDS) to monitor network traffic and sends alerts through a pipeline that involves the Fiware IoT Agent and Orion Context Broker, creating a real-time digital twin model of the LAN infrastructure. The visualizations of network conditions and intrusion status are provided through Grafana, while the mobile user interface is developed using Flutter, with Firebase providing backend data synchronization and notifications. Experimental results demonstrate the effectiveness of the system in detecting and reporting various types of network intrusions, offering enhanced network security through comprehensive monitoring and real-time alerting. This study contributes to the evolving field of LAN security by showcasing a practical implementation of digital twin technology in intrusion detection.
Description
Scholarly article
Keywords
Citation
Amusan, A. A., Adeniyi, E. O., & Amusan, E. A. (2024). Intrusion detection of local area network using digital twin technology. University of Pitesti Scientific Bulletin, Series: Electronics and Computer Science, 24(2), 21–32. Editura Universitatii din Pitesti.