Cyber Security and Critical Infrastructures, 2nd Edition

Topic Information

Dear Colleagues,

Critical infrastructures are vital assets for public safety, economic welfare, and the national security of countries. The vulnerabilities of these infrastructures have increased with the widespread use of information technologies. As critical national infrastructures are becoming more vulnerable to cyberattacks, their protection is becoming a significant issue for every organization as well as nation. The risks to continued operations from failing to upgrade aging infrastructures or not meeting mandated regulatory regimes are considered higher given the demonstrable impact of such circumstances.

Due to the rapid increase in sophisticated cyber threats targeting critical infrastructures with significant destructive effects, the cyber security of these infrastructures has become an agenda item for academics, practitioners, and policy makers. A holistic view which covers technical, policy, human, and behavioral aspects is essential to handle the cyber security of critical infrastructures effectively. Moreover, the ability to attribute crimes to criminals is a vital element of avoiding impunity in cyberspace. Moreover, the coronavirus pandemic has created new challenges for businesses as they adapt to an operating model in which working from home has become the ‘new normal’. Companies are accelerating their digital transformation, and cybersecurity is now a major concern.

The aim of this Topic is to gather both research and practical aspects of cyber security considerations in critical infrastructures. Aligned with the interdisciplinary nature of cyber security, authors from academia, government, and industry are welcome to contribute.

We seek original and high-quality submissions on one or more of the following topics (among others):

• Security of supervisory control and data acquisition (SCADA) systems;
• Cyber security of complex and distributed critical infrastructures;
• Cyber security of industrial control systems;
• Cyber security modeling and simulation;
• Cyber threat modeling and analysis;
• Safety–security interactions;
• Cyber security engineering;
• Behavioral modeling;
• Network security and protocols;
• Security, privacy, and legal issues of big data and the Internet of Things;
• Cyber threat intelligence;
• Situational awareness;
• Attack modeling, prevention, mitigation, and defense;
• Cyber-physical system security approaches and algorithms;
• Critical infrastructure security policies, standards, and regulations;
• Vulnerability and risk assessment methodologies for distributed critical infrastructures;
• Risk management and cyber insurance;
• Simulation and test beds for the security evaluation of critical infrastructures;
• Resiliency and security of cyber systems;
• Cyber security and privacy policies;
• Hardware security solutions;
• Incident response;
• Encryption, authentication, availability assurance;
• Human awareness and training;
• Intrusion detection;
• Trust and privacy preservation;
• Secure communication protocols;
• Malware analysis;
• Attribution of cyberattacks;
• Cyber warfare and peacekeeping;
• Hybrid war;
• Blockchain technology;
• Supply chain security;
• Ransomware;
• Post-quantum crypto;
• Zero-trust;
• Supply chain attacks.

Dr. Mohamed Amine Ferrag
Prof. Dr. Leandros Maglaras
Prof. Dr. Helge Janicke
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400
Future Internet futureinternet	2.8	7.1	2009	13.1 Days	CHF 1600
Journal of Cybersecurity and Privacy jcp	-	5.3	2021	32.4 Days	CHF 1000
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600

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Related Topic

Published Papers (6 papers)

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All Applied Sciences Electronics Future Internet JCP Sensors

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17 pages, 3126 KiB  

Open AccessArticle

Open DGML: Intrusion Detection Based on Open-Domain Generation Meta-Learning

by Kaida Jiang, Futai Zou, Hongjun Huang, Liwen Zheng and Haochen Zhai

Appl. Sci. 2024, 14(13), 5426; https://doi.org/10.3390/app14135426 - 22 Jun 2024

Viewed by 558

Abstract

Network security is crucial for national infrastructure, but the increasing number of network intrusions poses significant challenges. To address this issue, we propose Open DGML, a framework based on open-domain generalization meta-learning for intrusion detection. Our approach incorporates flow imaging, data augmentation, and [...] Read more.

Network security is crucial for national infrastructure, but the increasing number of network intrusions poses significant challenges. To address this issue, we propose Open DGML, a framework based on open-domain generalization meta-learning for intrusion detection. Our approach incorporates flow imaging, data augmentation, and open-domain generalization meta-learning algorithms. Experimental results on the ISCX2012, NDSec-1, CICIDS2017, and CICIDS2018 datasets demonstrate the effectiveness of Open DGML. Compared to state-of-the-art models (HAST-IDS, CLAIRE, FC-Net), Open DGML achieves higher accuracy and detection rates. In closed-domain settings, it achieves an average accuracy of 96.52% and a detection rate of 97.04%. In open-domain settings, it achieves an average accuracy of 68.73% and a detection rate of 61.49%. These results highlight the superior performance of Open DGML, particularly in open-domain scenarios, for effective identification of various network attacks. Full article

(This article belongs to the Topic Cyber Security and Critical Infrastructures, 2nd Edition)

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16 pages, 1166 KiB  

Open AccessArticle

Immunity-Empowered Collaboration Security Protection for Mega Smart Cities

by Kun Lan, Jianhua Li, Wenkai Huang and Gaolei Li

Electronics 2024, 13(11), 2001; https://doi.org/10.3390/electronics13112001 - 21 May 2024

Viewed by 876

Abstract

The cyberphysical systems of smart cities are facing increasingly severe attack situations, and traditional separate protection methods are difficult to effectively respond to. It is urgent to coordinate public safety and cybersecurity protection. However, the integration of the two faces many challenges and [...] Read more.

The cyberphysical systems of smart cities are facing increasingly severe attack situations, and traditional separate protection methods are difficult to effectively respond to. It is urgent to coordinate public safety and cybersecurity protection. However, the integration of the two faces many challenges and is a very promising research field. The aim of this study is to investigate technical approaches for the synergy between public safety and cybersecurity. This paper proposes a smart city safety protection model inspired by the human immune mechanism. It was found that through a three-line defense architecture similar to the human immune mechanism, and with the help of certain algorithms and functional middleware modules, public safety and cybersecurity protection components can be dynamically combined to achieve collaboration. This work has verified through experiments a valuable path to effectively resist complicated attack threats intertwined with public safety and cybersecurity factors. Full article

(This article belongs to the Topic Cyber Security and Critical Infrastructures, 2nd Edition)

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16 pages, 2561 KiB  

Open AccessArticle

A Novel Security Risk Analysis Using the AHP Method in Smart Railway Systems

by İsa Avcı and Murat Koca

Appl. Sci. 2024, 14(10), 4243; https://doi.org/10.3390/app14104243 - 16 May 2024

Cited by 3 | Viewed by 1090

Abstract

Transportation has an essential place in societies and importance to people in terms of its social and economic aspects. Innovative rail systems need to be integrated with developing technologies for transportation. Systemic failures, personnel errors, sabotage, and cyber-attacks in the techniques used will [...] Read more.

Transportation has an essential place in societies and importance to people in terms of its social and economic aspects. Innovative rail systems need to be integrated with developing technologies for transportation. Systemic failures, personnel errors, sabotage, and cyber-attacks in the techniques used will cause a damaged corporate reputation and revenue losses. In this study, cybersecurity attack methods in smart rail systems were determined, and cyber events occurring worldwide through these technologies were analyzed. Risk analysis in terms of transportation safety in smart rail systems was determined by considering the opinions of 10 different experts along with the Analytic Hierarchical Process (AHP) performance criteria. Informatics experts were selected from a group of people with at least 5–15 years of experience. According to these risk analysis calculations, cybersecurity stood out as the most critical security risk at 27.74%. Other risky areas included physical security, calculated at 14.59%, operator errors at 16.20%, and environmental security at 10.93%. Full article

(This article belongs to the Topic Cyber Security and Critical Infrastructures, 2nd Edition)

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12 pages, 808 KiB  

Open AccessArticle

Design of a Trusted Content Authorization Security Framework for Social Media

by Jiawei Han, Qingsa Li, Ying Xu, Yan Zhu and Bingxin Wu

Appl. Sci. 2024, 14(4), 1643; https://doi.org/10.3390/app14041643 - 18 Feb 2024

Viewed by 1516

Abstract

Artificial intelligence-generated content (AIGC) technology has had disruptive results in AI, representing a new trend in research and application and promoting a new era of AI. The potential benefits of this technology are both profound and diverse. However, the benefits of generative tools [...] Read more.

Artificial intelligence-generated content (AIGC) technology has had disruptive results in AI, representing a new trend in research and application and promoting a new era of AI. The potential benefits of this technology are both profound and diverse. However, the benefits of generative tools are accompanied by a series of significant challenges, the most critical of which is that it may cause AI information pollution on social media and mislead the public. Traditional network security models have shown their limitations in dealing with today’s complex network threats, so ensuring that generated content published on social media accurately reflects the true intentions of content creators has become particularly important. This paper proposes a security framework called “secToken”. The framework adopts multi-level security and privacy protection measures. It combines deep learning and network security technology to ensure users’ data integrity and confidentiality while ensuring credibility of the published content. In addition, the framework introduces the concept of zero trust security, integrates OAuth2.0 ideas, and provides advanced identity authentication, fine-grained access control, continuous identity verification, and other functions, to comprehensively guarantee the published content’s reliability on social media. This paper considers the main issues of generative content management in social media and offers some feasible solutions. Applying the security framework proposed in this paper, the credibility of generated content published on social media can be effectively ensured and can help detect and audit published content on social media. At the operational level, when extracting key information summaries from user-generated multimodal artificial intelligence-generated content and binding them to user identity information as a new token to identify user uniqueness, it can effectively associate user identity information with the current network status and the generated content to be published on the platform. This method significantly enhances system security and effectively prevents information pollution caused by generative artificial intelligence on social media platforms. This innovative method provides a powerful solution for addressing social and ethical challenges and network security issues. Full article

(This article belongs to the Topic Cyber Security and Critical Infrastructures, 2nd Edition)

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18 pages, 4026 KiB  

Open AccessArticle

Enhancing Cloud Security—Proactive Threat Monitoring and Detection Using a SIEM-Based Approach

by Emmanuel Tuyishime, Titus C. Balan, Petru A. Cotfas, Daniel T. Cotfas and Alexandre Rekeraho

Appl. Sci. 2023, 13(22), 12359; https://doi.org/10.3390/app132212359 - 15 Nov 2023

Cited by 5 | Viewed by 3569

Abstract

With the escalating frequency of cybersecurity threats in public cloud computing environments, there is a pressing need for robust security measures to safeguard sensitive data and applications. This research addresses growing security concerns in the cloud by proposing an innovative security information and [...] Read more.

With the escalating frequency of cybersecurity threats in public cloud computing environments, there is a pressing need for robust security measures to safeguard sensitive data and applications. This research addresses growing security concerns in the cloud by proposing an innovative security information and event management system (SIEM) that offers automated visibility of cloud resources. Our implementation includes a virtual network comprising virtual machines, load balancers, Microsoft Defender for Cloud, and an application gateway that functions as a web application firewall (WAF). This WAF scans incoming Internet traffic and provides centralized protection against common exploits and vulnerabilities, securing web applications within the cloud environment. We deployed the SIEM system to automate visibility and incident response for cloud resources. By harnessing the power of this employed SIEM, the developed system can continuously monitor, detect security incidents, and proactively mitigate potential security threats. Microsoft Defender for Cloud consistently assesses the configuration of cloud resources against industry standards, regulations, and benchmarks to ensure compliance requirements are met. Our findings highlight the practicality and effectiveness of deploying such solutions to safeguard cloud resources, offering valuable insights to organizations and security professionals seeking sustainable and resilient security measures in the cloud computing environment. Full article

(This article belongs to the Topic Cyber Security and Critical Infrastructures, 2nd Edition)

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18 pages, 1372 KiB  

Open AccessArticle

Navigating through Noise Comparative Analysis of Using Convolutional Codes vs. Other Coding Methods in GPS Systems

by Nawras H. Sabbry and Alla Levina

Appl. Sci. 2023, 13(20), 11164; https://doi.org/10.3390/app132011164 - 11 Oct 2023

Viewed by 1323

Abstract

This research highlights the importance of error-correcting codes in ensuring secure and efficient data transmission over noisy channels. This paper aims to address the issue of limited information regarding the factors that contribute to the effectiveness of the implementation of Convolutional Codes in [...] Read more.

This research highlights the importance of error-correcting codes in ensuring secure and efficient data transmission over noisy channels. This paper aims to address the issue of limited information regarding the factors that contribute to the effectiveness of the implementation of Convolutional Codes in GPS systems. The research problem revolves around the insufficiency of scholarly sources elucidating the rationale behind the utilization of convolutional codes, specifically in GPS systems, rather than others. Through an in-depth analysis of these factors, this study strives to achieve a comprehensive understanding of the application of Convolutional Codes in GPS. To tackle this research problem, a novel methodology involving comparative analysis is employed. The coding techniques commonly used in satellite communication systems (such as BCH, LDPC, and turbo codes) are carefully examined and compared to the advantages and suitability of Convolutional codes and the Viterbi algorithm for GPS systems. Each coding technique is evaluated based on factors such as error detection and correction capabilities, bandwidth efficiency, computational complexity, and resilience to noise. The key findings of this study shed light on the unique advantages offered by Convolutional codes and the Viterbi algorithm for GPS systems. The analysis reveals that these coding techniques exhibit superior error detection and correction capabilities, efficient bandwidth utilization, and the ability to withstand noise in the GPS communication channel. The results also highlight the computational complexity associated with these techniques, providing valuable insights for the implementation of convolutional codes in GPS systems. Overall, this article contributes to the existing knowledge by providing a comprehensive understanding of the reasons behind the suitability of convolutional codes for GPS systems. The findings of this study serve as a resource for researchers, engineers, and practitioners in the field of satellite communication, aiding in the comprehensive understanding, advancement, and optimization of GPS system designs. Full article

(This article belongs to the Topic Cyber Security and Critical Infrastructures, 2nd Edition)

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