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Applied Sciences
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Safety, Security and Privacy in Cyber-Physical Systems (CPS)
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Safety, Security and Privacy in Cyber-Physical Systems (CPS)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 2983

Special Issue Editors

Dr. Sohag Kabir

E-Mail Website
Guest Editor
Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK
Interests: safety; reliability; Internet of Things; autonomous systems; cyber security
Special Issues, Collections and Topics in MDPI journals
Dr. Ibrahim Ghafir

E-Mail Website
Guest Editor
Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK
Interests: cyber security; artificial intelligence; Internet of Things; machine learning; security in cyber physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite you to contribute to this Special Issue entitled “Safety, Security and Privacy in Cyber-Physical Systems (CPS)”

Applications of cyber-physical systems (CPS) are becoming increasingly common across the field of engineered systems, from cars and drones to manufacturing systems and medical devices, addressing prevailing societal changes and, increasingly, consumer demand. While CPSs offer enormous economic, societal, and innovation potential, they have opened new avenues for safety, security, and privacy concerns that can adversely affect our lives and the environment. Due to a large number of connected devices and their ability to control critical physical assets, deliberate cyber attacks and/or random failure events such as the mechanical failure of devices, communication failure, and unforeseen bad interactions between connected systems can cause CPSs to enter unsafe and dangerous physical states. The volume of attacks on CPSs is constantly increasing and attack space is evolving frequently. Therefore, there is a pressing need to develop innovative techniques and methods to address safety, security, and privacy concerns in the new generation of CPSs, thus assuring that they do not pose an unacceptable level of risk.

This Special Issue aims to publish work on multidisciplinary research for novel approaches, visionary ideas, experiences in tools and technologies, and case studies to address the challenges of safety, security, and privacy for cyber-physical systems. Both review articles and novel research papers are solicited.

Topics of interest include, but are not limited to:

  • Safety assurance of CPSs
  • Security/privacy/trust issues in CPSs
  • Cyber-physical threats and vulnerability analysis
  • Intrusion detection for CPSs
  • Artificial intelligence for the safety, security, and privacy of CPSs
  • Machine learning for the safety and security of CPSs
  • Threat modeling for CPSs
  • Privacy-enhanced technology in CPSs
  • Model-based safety analysis, design, and assessment
  • Blockchain for the safety and security of CPSs
  • Safety/security co-analysis and risk assessment
  • Reliability analysis of CPSs

Dr. Sohag Kabir
Dr. Ibrahim Ghafir
Guest Editors

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. Applied Sciences 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 2400 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

  • safety
  • security
  • privacy
  • cyber-physical systems
  • cyber security
  • internet of things
  • intrusion detection
  • artificial intelligence
  • machine learning

Published Papers (3 papers)

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Research

14 pages, 1562 KiB  
Article
Adversarial Attacks with Defense Mechanisms on Convolutional Neural Networks and Recurrent Neural Networks for Malware Classification
by Sharoug Alzaidy and Hamad Binsalleeh
Appl. Sci. 2024, 14(4), 1673; https://doi.org/10.3390/app14041673 - 19 Feb 2024
Viewed by 644
Abstract
In the field of behavioral detection, deep learning has been extensively utilized. For example, deep learning models have been utilized to detect and classify malware. Deep learning, however, has vulnerabilities that can be exploited with crafted inputs, resulting in malicious files being misclassified. [...] Read more.
In the field of behavioral detection, deep learning has been extensively utilized. For example, deep learning models have been utilized to detect and classify malware. Deep learning, however, has vulnerabilities that can be exploited with crafted inputs, resulting in malicious files being misclassified. Cyber-Physical Systems (CPS) may be compromised by malicious files, which can have catastrophic consequences. This paper presents a method for classifying Windows portable executables (PEs) using Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). To generate malware executable adversarial examples of PE, we conduct two white-box attacks, Jacobian-based Saliency Map Attack (JSMA) and Carlini and Wagner attack (C&W). An adversarial payload was injected into the DOS header, and a section was added to the file to preserve the PE functionality. The attacks successfully evaded the CNN model with a 91% evasion rate, whereas the RNN model evaded attacks at an 84.6% rate. Two defense mechanisms based on distillation and training techniques are examined in this study for overcoming adversarial example challenges. Distillation and training against JSMA resulted in the highest reductions in the evasion rates of 48.1% and 41.49%, respectively. Distillation and training against C&W resulted in the highest decrease in evasion rates, at 48.1% and 49.9%, respectively. Full article
(This article belongs to the Special Issue Safety, Security and Privacy in Cyber-Physical Systems (CPS))
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24 pages, 3044 KiB  
Article
Privacy-Preserving Deep Learning Framework Based on Restricted Boltzmann Machines and Instance Reduction Algorithms
by Alya Alshammari and Khalil El Hindi
Appl. Sci. 2024, 14(3), 1224; https://doi.org/10.3390/app14031224 - 01 Feb 2024
Viewed by 864
Abstract
The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource restrictions concerns that must be properly addressed for successful implementation in real-world [...] Read more.
The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource restrictions concerns that must be properly addressed for successful implementation in real-world CPS systems. Various privacy-preserving techniques have been proposed, but they often add complexity and decrease accuracy and utility. In this paper, we propose a privacy-preserving deep learning framework that combines Instance Reduction Techniques (IR) and the Restricted Boltzmann Machine (RBM) to preserve privacy while overcoming the limitations of other frameworks. The RBM encodes training data to retain relevant features, and IR selects the relevant encoded instances to send to the server for training. Privacy is preserved because only a small subset of the training data is sent to the server. Moreover, it is sent after encoding it using RBM. Experiments show that our framework preserves privacy with little loss of accuracy and a substantial reduction in training time. For example, using our framework, a CNN model for the MNIST dataset achieves 96% accuracy compared to 99% in a standard collaborative framework (with no privacy measures taken), with training time reduced from 133.259 s to 99.391 s. Our MLP model for MNIST achieves 97% accuracy compared to 98% in the standard collaborative framework, with training time reduced from 118.146 s to 87.873 s. Compared to other studies, our method is a simple approach that protects privacy, maintains the utility of deep learning models, and reduces training time and communication costs. Full article
(This article belongs to the Special Issue Safety, Security and Privacy in Cyber-Physical Systems (CPS))
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31 pages, 4610 KiB  
Article
ICVTest: A Practical Black-Box Penetration Testing Framework for Evaluating Cybersecurity of Intelligent Connected Vehicles
by Haichun Zhang, Jie Wang, Yijie Wang, Minfeng Li, Jinghan Song and Zhenglin Liu
Appl. Sci. 2024, 14(1), 204; https://doi.org/10.3390/app14010204 - 25 Dec 2023
Viewed by 843
Abstract
Intelligent connected vehicles (ICVs) are equipped with extensive electronic control units which offer convenience but also pose significant cybersecurity risks. Penetration testing, recommended in ISO/SAE 21434 “Road vehicles—Cybersecurity engineering”, is an effective approach to identify cybersecurity vulnerabilities in ICVs. However, there is limited [...] Read more.
Intelligent connected vehicles (ICVs) are equipped with extensive electronic control units which offer convenience but also pose significant cybersecurity risks. Penetration testing, recommended in ISO/SAE 21434 “Road vehicles—Cybersecurity engineering”, is an effective approach to identify cybersecurity vulnerabilities in ICVs. However, there is limited research on vehicle penetration testing from a black-box perspective due to the complex architecture of ICVs. Additionally, no penetration testing framework has been proposed to guide security testers on conducting penetration testing for the whole vehicle. The lack of framework guidance results in the inexperienced security testers being uncertain about the processes to follow for conducting penetration testing. Moreover, the inexperienced security testers are unsure about which tests to perform in order to systematically evaluate the vehicle’s cybersecurity. To enhance the penetration testing efficiency of ICVs, this paper presents a black-box penetration testing framework, ICVTest. ICVTest proposes a standardized penetration testing process to facilitate step-by-step completion of the penetration testing, thereby addressing the issue of inexperienced testers lacking guidance on how to initiate work when confronted with ICV. Also, ICVTest includes 10 sets of test cases covering hardware and software security tests. Testers can select appropriate test cases based on the specific cybersecurity threats faced by the target object, thereby reducing the complexity of penetration testing tasks. Furthermore, we have developed a vehicle cybersecurity testing platform for ICVTest that seamlessly integrates various testing tools. The platform enables even novice testers to conduct vehicle black-box penetration testing in accordance with the given guidance which addresses the current industry’s challenge of an overwhelming number of testing tasks coupled with a shortage of skilled professionals. For the first time, we propose a comprehensive black-box penetration testing framework and implement the framework in the form of a cybersecurity testing platform. We apply ICVTest to evaluate an electric vehicle manufactured in 2021 for assessing the framework’s availability. With the aid of ICVTest, even testers with limited experience in automotive penetration can effectively evaluate the security risks of ICVs. In our experiments, numerous cybersecurity vulnerabilities were identified involving in-vehicle sensors, remote vehicle control systems, and in-vehicle controller area network (CAN) bus. Full article
(This article belongs to the Special Issue Safety, Security and Privacy in Cyber-Physical Systems (CPS))
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