Autors: Radoglou-Grammatikis, P., Sarigiannidis, P., Efstathopoulos, G., Lagkas, T., Sarigiannidis, A., Mladenov, V. M., Siaxabanis, N.
Title: Defending Industrial Internet of Things Against Modbus/TCP Threats: A Combined AI-Based Detection and SDN-Based Mitigation Solution
Keywords: Active Learning, Intrusion Detection and Prevention, Modbus,

Abstract: The rapid evolution of the Industrial Internet of Things (IIoT) introduces multiple benefits, such as self-monitoring, pervasive control and self-healing, leading IIoT environments, such as Critical Infrastructures (CIs) to a new digital era. However, this new reality creates severe cybersecurity issues due to the vulnerable nature of the IIoT protocols. In this paper, we focus on Modbus/Transmission Control Protocol (TCP), which is widely adopted by IIoT environments, such as the smart electrical grid. Modbus/TCP is characterised by severe vulnerabilities since it does not incorporate adequate authentication and authorisation services. First, we provide a Modbus/TCP threat model, which estimates the severity of the Modbus/TCP threats supported by relevant Modbus/TCP-related penetration testing tools. The proposed model combines (a) STRIDE-per-element, (b) an Attack Defence Tree (ADT) and (c) one from Common Vulnerability Scoring System (CVSS) and OWASP Risk Rating (OWASP-RR) methodol


    Issue, pp. 1-44, 2022, United States,

    Цитирания (Citation/s):
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    Вид: статия в списание, публикация в реферирано издание, индексирана в Google Scholar