Faculty of Computer Science and Engineering
Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5
The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest
faculties in that field in the region.
The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.
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Item type:Publication, Observable and Reproducible Sandbox Environment for Cybersecurity Research(IEEE, 2026-07); ; The availability of controlled sandbox environments from which relevant network traces and system logs can be obtained is a prerequisite for studying network attacks. Such data is also essential for the development of novel security tools as well as for evaluating the effectiveness of various incident response strategies. A persistent challenge faced by security researchers is how to set up these environments in a consistent manner, guaranteeing the integrity of the underlying infrastructure while still providing meaningful data. In this paper we present the design of a secure, reproducible, and observable sandbox in which purposefully vulnerable applications are deployed and exploited. Every interaction is monitored in real time using strategically placed network probes, performance monitoring agents, and log shippers. The use of modern configuration management tools eliminates configuration drift and entropy, guaranteeing reproducibility of the deployed scenarios. We describe practical approaches for implementing the sandbox, detailing the tools and methods required for real-world deployment. Using a recent sandbox instantiation as an example, we demonstrate how the acquired network data can be used to identify attack patterns, analyze defense strategies, and assess overall incident response effectiveness. The obtained dataset is open-sourced and can be reused to support further research. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A STUDENT-CENTERED APPROACH FOR FOCUS ENHANCING BY SUPPORTING SELF-REGULATED LEARNING THROUGH A MOBILE APPLICATION(IATED, 2026-07-25); Madevska Bogdanova, Ana - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comprehensive overview of quantum serverless: Elastic integration of quantum and classical computing(Elsevier BV, 2026-10); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, MetriKG: Profiling Static and Evolving Knowledge Graphs(ACM, 2026-05-28); Hose, KatjaKnowledge graphs (KGs) are a foundational technology for representing and integrating information across heterogeneous domains. As some KGs evolve, understanding how their structural and semantic properties change over time is crucial for ensuring quality, consistency, and interpretability. Existing methods for KG evaluation often focus on static graphs or analyze evolution solely at the data level, leaving schema-level dynamics underexplored. To address this gap, we introduce MetriKG, a web-based application that computes a comprehensive set of metrics for both static and evolving KGs. MetriKG enables users to evaluate KGs provided as RDF files or through SPARQL endpoints, allowing for multi-dimensional analysis of aspects such as cohesion, connectivity, and inheritance depth. By supporting metric computation at both data and schema levels, MetriKG allows for systematic profiling, classification, and temporal monitoring of KGs. MetriKG is open-source and publicly available. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, MetriKG: Profiling Static and Evolving Knowledge Graphs(ACM, 2026-05-28); Hose, Katja - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Privacy preserving synchronization of directed dynamical networks with periodic data-sampling(Elsevier BV, 2025-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ANTI-VIRUS TOOLS ANALYSIS USING DEEP WEB MALWARES(AIRCC Publication Corporation, 2018-12-22); ; Knowledge about the strength of the anti-virus engines (i.e. tools) to detect malware files on the Deep web is important for people and companies to devise proper security polices and to choose the proper tool in order to be more secure. In this study, using malware file set crawled from the Deep web we detect similarities and possible groupings between plethora of anti-virus tools (AVTs) that exist on the market. Moreover, using graph theory, data science and visualization we find which of the existing AVTs has greater advantage in detecting malware over the other AVTs, in a sense that the AVT detects many unique. Finally, we propose a solution, for the given malware set, what is the best strategy for a company to defend against malwares if it uses a multi-scanning approach. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Interplay Between Spreading and Random Walk Processes in Multiplex Networks(Institute of Electrical and Electronics Engineers (IEEE), 2017-10); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, VulnerSec: A Flexible, Automated and Open-Source Cybersecurity Framework(Faculty of Computer Science and Engineering, 2025) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Engaging Students with Personalized and Remotely Orchestrated Cybersecurity Training Exercises(Faculty of Computer Science and Engineering, 2021)