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AI-Driven Cybersecurity and Reliability Analysis for Critical Infrastructures
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AI-Driven Cybersecurity and Reliability Analysis for Critical Infrastructures

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (25 August 2023) | Viewed by 6630

Special Issue Editors

Dr. Uttam Ghosh

E-Mail Website
Guest Editor
Department of Applied Mathematics, University of Calcutta, Kolkata, West Bengal, India
Interests: cloud computing; edge computing; cyber-physical systems; security; IoT; energy-aware sustainable computing
Special Issues, Collections and Topics in MDPI journals
Dr. Amrit Mukherjee

E-Mail Website
Guest Editor
Institute of Applied Informatics, Department of Computer Science, University of South Bohemia, České Budějovice, Czech Republic
Interests: artificial intelligence; next-generation IoT systems; wireless sensor networks; cognitive radio; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The implementation of the Internet of Things (IoT) in critical infrastructures(CI) such as the smart power grid has seen exponential growth. Thus, it is not surprising that many recent cyber-attacks are IoT-enabled; the attacker initially exploits vulnerable IoT technology and then breaches a critical device that is linked to it in some way. Such attacks present a serious threat for certain industries, such as smart grids, transportation, and medical services, as IoT technologies constitute part of essential backend systems in these areas.

This Special Issue aims to address research on the role of IoT in critical infrastructures. As the capabilities of the distributed systems grow, data can be stored locally with the edge without affecting the transmission of private data. Processing consumer data locally will restrict its use by the machine learning model, a process known as federated learning. In this category of learning, models are trained and deployed directly in remote devices with respect to maintaining consumer data locally.  “Device” here refers to any distributed system with the leverage to deliver the benefits of federated learning.

Dr. Uttam Ghosh
Dr. Amrit Mukherjee
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • cybersecurity
  • AI
  • critical infrastructure
  • smart grid
  • reliability analysis
  • wearable medical device

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Published Papers (1 paper)

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Research

17 pages, 4208 KiB  
Article
Predicting Cybersecurity Threats in Critical Infrastructure for Industry 4.0: A Proactive Approach Based on Attacker Motivations
by Adel Alqudhaibi, Majed Albarrak, Abdulmohsan Aloseel, Sandeep Jagtap and Konstantinos Salonitis
Sensors 2023, 23(9), 4539; https://doi.org/10.3390/s23094539 - 6 May 2023
Cited by 18 | Viewed by 5943
Abstract
In Industry 4.0, manufacturing and critical systems require high levels of flexibility and resilience for dynamic outcomes. Industrial Control Systems (ICS), specifically Supervisory Control and Data Acquisition (SCADA) systems, are commonly used for operation and control of Critical Infrastructure (CI). However, due to [...] Read more.
In Industry 4.0, manufacturing and critical systems require high levels of flexibility and resilience for dynamic outcomes. Industrial Control Systems (ICS), specifically Supervisory Control and Data Acquisition (SCADA) systems, are commonly used for operation and control of Critical Infrastructure (CI). However, due to the lack of security controls, standards, and proactive security measures in the design of these systems, they have security risks and vulnerabilities. Therefore, efficient and effective security solutions are needed to secure the conjunction between CI and I4.0 applications. This paper predicts potential cyberattacks and threats against CI systems by considering attacker motivations and using machine learning models. The approach presents a novel cybersecurity prediction technique that forecasts potential attack methods, depending on specific CI and attacker motivations. The proposed model’s accuracy in terms of False Positive Rate (FPR) reached 66% with the trained and test datasets. This proactive approach predicts potential attack methods based on specific CI and attacker motivations, and doubling the trained data sets will improve the accuracy of the proposed model in the future. Full article
(This article belongs to the Special Issue AI-Driven Cybersecurity and Reliability Analysis for Critical Infrastructures)
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