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Cyber Attacks in Industrial Control Systems
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Cyber Attacks in Industrial Control Systems

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

Deadline for manuscript submissions: 20 May 2024 | Viewed by 1692

Special Issue Editor

Dr. Chuadhry Mujeeb Ahmed

E-Mail Website
Guest Editor
School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: security; cyber security; cyber physical systems; sensing systems security; sensor security; attacks on sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce the call for papers for this Special Issue on "Cyber Attacks in Industrial Control Systems". This Special Issue explores the growing concern and challenges posed by cyber-attacks targeting industrial control systems (ICS) as critical components of various industries such as energy, manufacturing, transportation, and healthcare.

Industrial control systems are increasingly interconnected with information technology (IT) systems, which exposes them to potential cyber threats. Cyber attacks on ICS can have severe consequences, including the disruption of operations, compromised safety, financial losses, and even threats to human life. Therefore, understanding and addressing the vulnerabilities, risks, and countermeasures related to ICS cyber attacks is of paramount importance.

We invite researchers, academics, and industry experts to contribute their original research, case studies, and review articles to this Special Issue. The topics of interest include, but are not limited to:

  1. The threat landscape and emerging trends in cyber attacks on industrial control systems.
  2. A vulnerability analysis and risk assessment of ICS components and networks.
  3. Intrusion detection and prevention techniques for ICS environments.
  4. Secure protocols and communication frameworks for protecting ICS from cyber attacks.
  5. Incident response and recovery strategies in the aftermath of ICS cyber attacks.
  6. Case studies and real-world experiences highlighting the challenges and lessons learned.
  7. Security standards, regulations, and best practices for securing industrial control systems.
  8. Emerging technologies and approaches for enhancing the resilience of ICS against cyber attacks.

In bringing together diverse perspectives and cutting-edge research, this Special Issue aims to comprehensively understand the threat landscape, vulnerabilities, and mitigation strategies related to cyber attacks in industrial control systems. We encourage submissions that contribute to theoretical advancements and practical implications for securing critical infrastructure

Dr. Chuadhry Mujeeb Ahmed
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • cyber attacks
  • cyber security
  • secure protocols

Published Papers (1 paper)

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Research

20 pages, 3048 KiB  
Article
Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems
by Saadin Oyucu, Onur Polat, Muammer Türkoğlu, Hüseyin Polat, Ahmet Aksöz and Mehmet Tevfik Ağdaş
Sensors 2024, 24(1), 155; https://doi.org/10.3390/s24010155 - 27 Dec 2023
Viewed by 1043
Abstract
Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due [...] Read more.
Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems. Full article
(This article belongs to the Special Issue Cyber Attacks in Industrial Control Systems)
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