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Sensors
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Cyber Physical System: Security and Resilience Challenges and Solutions
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Cyber Physical System: Security and Resilience Challenges and Solutions

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

Deadline for manuscript submissions: 10 February 2026 | Viewed by 3193

Special Issue Editors

Dr. Shareeful Islam

E-Mail Website
Guest Editor
School of Computing and Information Science, Anglia Ruskin University, Cambridge CB1 1PT, UK
Interests: cyber security risk management; threat intelligence; vulnerability assessment; AI enabled cyber security; incident response and business continuity; information security audit and assurance; cyber insurance
Special Issues, Collections and Topics in MDPI journals
Dr. Sotiris Ioannidis

E-Mail Website
Guest Editor
School of Electrical and Computer Engineering, Technical University of Crete, Akrotiri Campus, 731 00 Chania, Greece
Interests: systems and network security; security policy; privacy; high-speed networks
Special Issues, Collections and Topics in MDPI journals
Dr. Spyros Papastergiou

E-Mail Website
Guest Editor
Maggioli Group, 151 24 Athens, Greece
Interests: risk and threat assessment; critical infrastructure protection; audit; assurance; resilience
Prof. Dr. Marc-Oliver Pahl

E-Mail Website
Guest Editor
Institut Mines Télécom Atlantique, 2 Rue de La Châtaigneraie, 35510 Cesson-Sévigné, France
Interests: cyber security; virtual reality; critical networked Infrastructures; anomaly detection

Special Issue Information

Dear Colleagues,

Cyber Physical System (CPS) interconnects cyber and physical infrastructure that is now widely adopted in different sectors specifically the critical infrastructure sector, including smart grid, transport, healthcare, and others. CPS is inherently complex with heterogeneous infrastructure and adopts emerging technologies such as data analytics, Internet of Things (IoT), sensors, and cloud computing to support its operations. This certainly increases the efficiency of the service delivery, but at the same time, CPS has to face the known and unknown attack- surfaces that can be exploited for any potential risk that can even cause catastrophic consequences. There is a need not only to ensure the security of the overall CPS but also the survivability of the key functionalities regardless of a successful adverse attack. However, ensuring the security and resilience of CPS is a challenging tasks due to a number of reasons, notably the unique threats and attack patterns of sector-specific CPS, the evolving threat landscape, and the continuous evolution of the system and its surrounding context. In this context, this Special Issue aims to investigate state-of-the-art practices to tackle the security and resilience challenges of CPS.

This Special Issue will include methods, technologies, processes, solutions, and tools on the topic of security and resilience of CPS.

Topics of interest include, but are not limited to, the following:

  • Unique sector-specific attack pattern and defense strategies for CPS;
  • Dynamic risk management;
  • AI-enabled solutions for security and resilience;
  • Incident detection and response;
  • Resilience framework for CPS;
  • Threat intelligence and information sharing;
  • Threat modelling and vulnerability assessment;
  • Virtual reality-based simulation and evaluation;
  • Advanced penetration testing;
  • Cyber security certification scheme;
  • Best practice for legal compliance;
  • Conformity with AI and digital resilience act.

Dr. Shareeful Islam
Dr. Sotiris Ioannidis
Dr. Spyros Papastergiou
Prof. Dr. Marc-Oliver Pahl
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. 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

  • cybersecurity
  • resilience
  • cyber physical systems
  • risk management
  • vulnerability assessment
  • intrusion detection and response
  • incident response
  • AI and digital resilience act

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Published Papers (3 papers)

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Research

23 pages, 3925 KiB  
Article
An Edge-Computing-Based Integrated Framework for Network Traffic Analysis and Intrusion Detection to Enhance Cyber–Physical System Security in Industrial IoT
by Tamara Zhukabayeva, Zulfiqar Ahmad, Aigul Adamova, Nurdaulet Karabayev and Assel Abdildayeva
Sensors 2025, 25(8), 2395; https://doi.org/10.3390/s25082395 - 10 Apr 2025
Viewed by 396
Abstract
Industrial Internet of things (IIoT) environments need to implement reliable security measures because of the growth in network traffic and overall connectivity. Accordingly, this work provides the architecture of network traffic analysis and the detection of intrusions in a network with the help [...] Read more.
Industrial Internet of things (IIoT) environments need to implement reliable security measures because of the growth in network traffic and overall connectivity. Accordingly, this work provides the architecture of network traffic analysis and the detection of intrusions in a network with the help of edge computing and using machine-learning methods. The study uses k-means and DBSCAN techniques to examine the flow of traffic in a network and to discover several groups of behavior and possible anomalies. An assessment of the two clustering methods shows that K-means achieves a silhouette score of 0.612, while DBSCAN achieves 0.473. For intrusion detection, k-nearest neighbors (KNN), random forest (RF), and logistic regression (LR) were used and evaluated. The analysis revealed that both KNN and RF yielded seamless results in terms of precision, recall, and F1 score, close to the maximum possible value of 1.00, as demonstrated by both ROC and precision–recall curves. Accuracy matrices show that RF had better precision and recall for both benign and attacks, while KNN and LR had good detection with slight fluctuations. With the integration of edge computing, the framework is improved by real-time data processing, which means a lower latency of the security system. This work enriches the knowledge of the IIOT by offering a detailed solution to the issue of cybersecurity in IoT systems, based on well-grounded performance assessments and the right implementation of current technologies. The results thus support the effectiveness of the proposed framework to improve security and provide tangible improvements over current approaches by identifying potential threats within a network. Full article
(This article belongs to the Special Issue Cyber Physical System: Security and Resilience Challenges and Solutions)
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22 pages, 12123 KiB  
Article
Gyro-Mag: Attack-Resilient System Based on Sensor Estimation
by Sunwoo Lee
Sensors 2025, 25(7), 2208; https://doi.org/10.3390/s25072208 - 31 Mar 2025
Viewed by 180
Abstract
Several researchers recently demonstrated that attackers can interfere with an inertial measurement unit (IMU) sensor’s normal function or take complete control of sensor measurements by physically injecting malicious signals into the sensor. Although there are existing methods for detecting such signal injection attacks, [...] Read more.
Several researchers recently demonstrated that attackers can interfere with an inertial measurement unit (IMU) sensor’s normal function or take complete control of sensor measurements by physically injecting malicious signals into the sensor. Although there are existing methods for detecting such signal injection attacks, most do not provide resilience. Indeed, detection-only methods cannot respond when attacks have already occurred, which results in accidents such as crashes or falls. In this paper, we propose the first method that can detect signal injection attacks on IMU sensors based on the relation between the gyroscope and the magnetometer, and provide long-term resilience against these attacks. We construct a mathematical model to estimate one sensor’s data from the other’s data based on their relation. With this mathematical model, the device can detect signal injection attacks on the IMU sensor and continue to function in a near-normal state based on the estimated data. Our method can be easily adapted to deployed devices since it requires only estimation software and no additional hardware. We evaluated our method using a total of five open datasets and commercial devices. Our method has a resilience of 99.78% against signal injection attacks while consuming only reasonable computational costs. Full article
(This article belongs to the Special Issue Cyber Physical System: Security and Resilience Challenges and Solutions)
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24 pages, 2376 KiB  
Article
Adoption of Deep-Learning Models for Managing Threat in API Calls with Transparency Obligation Practice for Overall Resilience
by Nihala Basheer, Shareeful Islam, Mohammed K. S. Alwaheidi and Spyridon Papastergiou
Sensors 2024, 24(15), 4859; https://doi.org/10.3390/s24154859 - 26 Jul 2024
Cited by 2 | Viewed by 1621
Abstract
System-to-system communication via Application Programming Interfaces (APIs) plays a pivotal role in the seamless interaction among software applications and systems for efficient and automated service delivery. APIs facilitate the exchange of data and functionalities across diverse platforms, enhancing operational efficiency and user experience. [...] Read more.
System-to-system communication via Application Programming Interfaces (APIs) plays a pivotal role in the seamless interaction among software applications and systems for efficient and automated service delivery. APIs facilitate the exchange of data and functionalities across diverse platforms, enhancing operational efficiency and user experience. However, this also introduces potential vulnerabilities that attackers can exploit to compromise system security, highlighting the importance of identifying and mitigating associated security risks. By examining the weaknesses inherent in these APIs using security open-intelligence catalogues like CWE and CAPEC and implementing controls from NIST SP 800-53, organizations can significantly enhance their security posture, safeguarding their data and systems against potential threats. However, this task is challenging due to evolving threats and vulnerabilities. Additionally, it is challenging to analyse threats given the large volume of traffic generated from API calls. This work contributes to tackling this challenge and makes a novel contribution to managing threats within system-to-system communication through API calls. It introduces an integrated architecture that combines deep-learning models, i.e., ANN and MLP, for effective threat detection from large API call datasets. The identified threats are analysed to determine suitable mitigations for improving overall resilience. Furthermore, this work introduces transparency obligation practices for the entire AI life cycle, from dataset preprocessing to model performance evaluation, including data and methodological transparency and SHapley Additive exPlanations (SHAP) analysis, so that AI models are understandable by all user groups. The proposed methodology was validated through an experiment using the Windows PE Malware API dataset, achieving an average detection accuracy of 88%. The outcomes from the experiments are summarized to provide a list of key features, such as FindResourceExA and NtClose, which are linked with potential weaknesses and related threats, in order to identify accurate control actions to manage the threats. Full article
(This article belongs to the Special Issue Cyber Physical System: Security and Resilience Challenges and Solutions)
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