About the Journal
The Journal of Telecommunications and Information Technology is published quarterly. It comprises original contributions, dealing with a wide range of topics related to telecommunications and information technology. All papers are peer-reviewed. The articles presented in JTIT focus primarily on experimental research results advancing scientific and technological knowledge about telecommunications and information technology.
Current Issue
No. Special Issue (2025)
We are pleased to present this special issue of the Journal of Telecommunications and Information Technology, which features selected scientific papers from the 63rd FITCE Congress (Federation of Telecommunications Engineers of the European Community), held in Kraków, Poland, in September 2024. The central theme of the Congress was "New Technologies and Services – Opportunities and Threats", explored through four thematic sessions focused on cybersecurity, technologies, and their applications. Additional special sessions were organized for young Ph.D. students and representatives of the telecommunications industry.
The articles presented in this issue address key challenges related to ensuring communication security and privacy, the application of machine learning and artificial intelligence methods, and the integration of best industry practices—topics of critical importance in today’s digital landscape. This special issue showcases research efforts aimed at solving technical problems through original systemic approaches that enhance the state-of-the-art in communication technologies.
The included articles address the following topics:
- The role of cybersecurity certification within the upcoming EUCC framework, with practical approaches to laboratory accreditation and penetration testing methodologies.
- Advanced machine learning-based detection of malware-generated domains (DGA), using both classical and neural models.
- Application of differential privacy in biomedical signal analysis, ensuring the secure and ethical use of patient data.
- Optimization of military ad hoc networks to reduce detectability via unmanned vehicles.
- Cyber and operational challenges associated with the shutdown of traditional PSTN systems and the transition to all-IP networks.
- A techno-economic analysis of cybersecurity of IoT and OT, including implications for policy and market regulation.
- Evaluation of resource allocation mechanisms in 6G V2X networks, with a focus on privacy and system performance trade-offs.
Each paper included in this issue has undergone a peer review process and was selected based on its originality, relevance, and potential impact on the telecommunications field.
As Guest Editors, it has been a privilege to oversee the development of this special issue. We were impressed by the breadth and quality of the submissions, which reflect not only the dynamic and evolving nature of the field but also the collaborative spirit of the FITCE community. We extend our sincere thanks to all the authors for their outstanding contributions and to the anonymous reviewers for their careful and constructive evaluations.
We also wish to express our gratitude to the JTIT Editorial Board for their support and to the FITCE 2024 Organizing Committee for hosting an exceptional and inspiring Congress in Kraków.
We hope this special issue will serve as a valuable reference for readers and inspire further research and innovation at the intersection of telecommunications, cybersecurity, and emerging digital technologies.
George Agapiou, Andy Valdar, and Piotr Zwierzykowski
Guest Editors
Published:
2025-05-14
Full Issue
ARTICLES FROM THIS ISSUE
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k-anonymity in Resource Allocation for Vehicle-to-Everything (V2X) Systems
Abstract
Sixth generation (6G) vehicle-to-everything (V2X) systems face numerous security threats, including Sybil and denial-of-service (DoS) cyber-attacks. To provide a secure exchange of data and protect users' identities in 6G V2X communication systems, anonymization techniques - such as k-anonymity - can be used. In this work, we study centralized vs. k-anonymity based resource allocation methods in a vehicular edge computing (VEC) network. Allocation decisions for vehicular networks are classically posed as a centralized optimization task. Therefore, an information flow is transmitted from the vehicles to the communication premises. In addition to a resource allocation decision, vehicle information is not required. We analyze the centralized allocation versus k-anonymous allocation models. To show a potential deterioration introduced by anonymity, we quantify the gap in the optimal goal in two cases: based on resource allocation and with aim at energy reduction. Our numerical results indicate that energy consumption rises by 1% in smaller scenarios and 23% in medium scenarios, whereas it decreases by 14% in larger scenarios.
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The Proactive Face of Cybersecurity: Certification. Legislation and Market Response from the Perspective of ITSEF
Abstract
The first European Cybersecurity Certification Scheme according to the Common Criteria (EUCC) specifies a number of additional requirements for Conformity Assessment Bodies (CABs) to be technically competent to provide evaluation and certification services. The NIT Testing Laboratory (ITSEF) has developed a roadmap to meet these requirements and obtain the status of an authorized ITSEF that can provide assessments of ICT products at the "high" assurance level. The roadmap consists of 3 parts: one organizational part concerning the management system and two technical parts concerning evaluations. The paper presents two action points: the innovative approach that NIT ITSEF has implemented regarding the integrated management system in the laboratory in order to achieve optimal cost-benefit ratios and the reliable and verifiable methodology for calculating the attack potential that NIT ITSEF has used to prove that the penetration tests developed and executed on the evaluated software product meet the requirements of AVA_VAN.5. The roadmap will fulfill all the requirements necessary to obtain the status of an authorized ITSEF in the EUCC program.
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Techno-economics of IoT and OT Security
Abstract
This paper provides an overview of the techno-economics of cybersecurity in IoT and OT devices. The purpose is to identify and provide justification for regulatory action within the area.
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The Potential Cyber and Network Security Issues of PSTN Closure
Abstract
Up until a few years ago, all phone calls over land lines, mobile networks, cable TV networks and many altnets used circuit-switching technology. This has been the case despite the massive build-up of packet-based data networks - and the dominance of Wi-Fi, broadband, and the Internet in people's lives over the last 20 years or so. Now all these network operators are engaged in shifting telephone service onto their packet-based data infrastructures and withdrawing the obsolescent circuit switched technology. This article considers why this change is happening, how calls will be handled in the future and the big challenges faced by landline operators in this transition, with special emphasize on the potential cyber and network security issues involved.
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Privacy-preserving Framework for Automated Detection of Arrhythmia in ECG Data
Abstract
The integration of machine learning in biomedical engineering applications is crucial to ensure user data security and privacy. This work explores anonymization and differential privacy (DP) frameworks to reduce the risk of biometric identification. The DP method is used to train models in biosignal data without compromising the diagnostic results. The proposed approach for privacy-preserving arrhythmia detection uses a machine learning diagnostic system that reduces discrepancies between prepossessed and raw data, maintaining a correct level of diagnostic precision while improving privacy. The application is evaluated using a control model to analyze the accuracy difference when using privacy-preserving input data.
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Enhancing DGA Detection with Machine Learning Algorithms
Abstract
The domain generation algorithm (DGA) is a popular technique used by malware to reliably establish a connection to a command and control (C&C) server. Pseudo-random domain names generated by DGA are used to bypass security measures and allow attackers to maintain control over malware-infected devices. In this work, we present a two-pronged approach to detecting character-based and word-based DGA domain names, creating classifiers specifically tailored to each type. For character-based DGA detection, we employed seven traditional machine learning methods: support vector machine, extremely randomized trees, logistic regression, Gaussian naive Bayes, nearest centroid, random forests, and k-nearest neighbors. We applied a featureful approach, using features extracted from the domain names themselves. Some of these features were drawn from existing literature, while others were newly proposed by authors. Feature selection techniques were used to retain only the best-performing ones. For the more complex task of detecting word-based DGA domain names, we used CNN and LSTM models, relying solely on word embeddings derived from the domain name components. Performance evaluation shows that proposed method gives high-performing, specialized DGA classifiers, which can be combined to create a more general-purpose classifier.
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Staying Hidden at Battlefields While Communicating via Unmanned Vehicles
Abstract
History shows that information is one of the key factors in military conflicts. During military conflicts, there is a need to maintain a communication channel on the battlefield while staying hidden from the enemy. In this paper, we present a simulator that allows to use a communication network and minimize the risk of being detected by the enemy. The simulator, using the Prim algorithm and fine-tuning, shows how a mobile ad-hoc network established between soldiers with the aid of unmanned vehicles, i.e. drones, may become undetectable for the enemy by properly optimizing drone positions.
