Dear Readers,

It gives me great pleasure to announce the second regular issue of 2026. I would like to thank all the authors for their sound research and the editorial board and guest reviewers for the extremely valuable reviews and suggestions for improvement. These contributions together with the support of the community enable us to run our journal and maintain its quality. 

I would still like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and emerging trends. 

In this regular issue, I am very pleased to present 6 accepted papers by 20 authors from 6 countries: Brazil, Germany, India, North Macedonia, Saudi Arabia, Türkiye.  

Gustavo Lazarotto Schroeder, Wesllei Felipe Heckler, Rosemary Francisco, and Jorge Luis Victória Barbosa from Brazil address in their manuscript the growing problem of problematic smartphone use (PSU) by proposing OntoKratos, an ontology-based approach that models contextual, demographic, and mental health information to identify PSU and recommend personalized interventions through semantic reasoning. The research contributes a formal and reusable ontology with SWRL-based inference mechanisms, demonstrating through simulated data that OntoKratos effectively classifies PSU states, infers risk factors, and generates evidence-based intervention suggestions. 

In a collaborative research between colleagues from North Macedonia and Germany, Aleksandar Velinov, Aleksandra Mileva, Simon Volpert, Sebastian Zillien, and Steffen Wendzel look into the steganographic analysis of different network protocols which becomes a necessary part of their security evaluation, to prevent their abuse as carriers of hidden messages. In this manuscript, twenty novel covert channels are identified in QUIC, with an accent on their transmission rate, undetectability, and robustness, suggested countermeasures, and one implemented covert channel as a proof-of-concept.

Hanan Hafiz and Maher Alharby from Saudi Arabia introduce in their work a study that aims to develop efficient machine learning models for detecting DDoS attacks in cloud environments by addressing challenges related to multi-tenant traffic patterns and virtualized infrastructure constraints. The main contributions of this study include binary and multiclass DDoS classification with feature selection, evaluation of model performance and computational efficiency, and mitigation of data imbalance using oversampling techniques.

Kausthav Pratim Kalita, Debojit Boro, and Dhruba Kumar Bhattacharyya from India investigate in their research the issue that big data platforms face limitations in centralized access control despite their distributed architecture and propose integrating blockchain technology using smart contracts to enable secure and controlled access to cluster resources. Through Ethereum-based simulations, the study demonstrates that appropriate indexing and hashing mechanisms can effectively enforce access control while maintaining acceptable execution cost and execution time.

Gamze Cabadag, Ali Degirmenci, and Omer Karal from Türkiye research in their work FFT-based radar frequency estimation errors arising from non-integer FFT bin alignment and evaluate twelve interpolation techniques under Gaussian and Laplace noise over varying SNRs and bandwidths. Monte Carlo analyses combined with FLOPs-based complexity evaluation show that the improved Quinn method achieves the highest estimation accuracy for both noise types, while simpler methods offer lower computational cost with reduced performance.

Last but not least, Mashael M. Alsulami, Kholoud Althobaiti and Haneen Algethami from Saudi Arabia address in their paper the limitation of traditional job recommendation systems by introducing JobMatcher, a multi-layered framework that combines content-based filtering-KNN, and large language model–based evaluation to better capture career context and progression. The findings show that utilizing ChatGPT as a refinement layer improves alignment with expert judgments, resulting in more relevant and realistic job recommendations.

Enjoy Reading!

Best regards,

Christian Gütl, Managing Editor-in-Chief

Graz University of Technology, Graz, Austria