Dr. Daniel Ioan Hunyadi

Securing Authentication and Detecting Malicious Entities in Drone Missions

Association rule mining plays a critical role in uncovering item correlations and hidden patterns within transactional data, particularly in e-commerce environments. Despite the widespread use of Apriori and FP-Growth algorithms, few studies offer a statistically rigorous, tool-based comparison of their performance on real-world e-commerce data. This paper addresses this gap by evaluating both algorithms in terms of execution time, memory consumption, rule generation volume, and rule strength (support, confidence, and lift). Implementations in RapidMiner and an analysis through SPSS establish statistically significant performance differences, particularly under varying support thresholds. Our findings confirm that FP-Growth consistently outperforms Apriori for large-scale datasets due to its ability to bypass candidate generation, while Apriori retains pedagogical and small-scale relevance. The study contributes practical guidance for data scientists and e-commerce practitioners choosing suitable rule-mining techniques based on their data size and performance constraints.

18 December 2024 in Drones

Journal Article

Are Digital Education Resources Meeting the Preliminary Needs of „R.E.D. Teacher EDucation” Project?

This study proposes a hierarchical communication framework for drone swarms designed to enhance security and operational efficiency. Leveraging elliptic curve cryptography and space quanta concepts, the model ensures continuous authentication and risk assessment of participating entities. Experimental results demonstrate the framework’s effectiveness in mitigating security risks, achieving reliable communication even in adverse conditions. Key findings include significant improvement in threat detection accuracy and reduced computational overhead, validating the model’s applicability for real-world drone swarm operations. These contributions establish a robust foundation for secure and resilient drone coordination.

4 December 2024 in Revista Romaneasca pentru Educatie Multidimensionala

Journal Article

This study evaluates the preparedness of Romanian public pre-university teachers for digital pedagogy training as part of the R.E.D. Teacher Education project, which aims to enhance lifelong learning and educational quality, in alignment with European Union (EU) standards and Sustainable Development Goal 4 (SDG 4). Data were collected from 1,864 educators through a structured questionnaire aligned with global digital competency frameworks. Results indicate a strong interest in digital tools, though challenges remain, particularly in fostering collaboration and ensuring equitable access to digital resources. The study concludes that targeted professional development is needed to enhance teachers' digital literacy and address the digital divide. Key recommendations include improving access to digital platforms, promoting collaboration, and ensuring responsible use of digital resources to support inclusive, equitable education.

4 July 2025 in Entropy

Journal Article

Mining Complex Ecological Patterns in Protected Areas: An FP-Growth Approach to Conservation Rule Discovery

Temperate Blind Signature Scheme for Particular Subspaces

Cristina Cismaș

This study introduces a data-driven framework for enhancing the sustainable management of fish species in Romania’s Natura 2000 protected areas through ecosystem modeling and association rule mining (ARM). Drawing on seven years of ecological monitoring data for 13 fish species of ecological and socio-economic importance, we apply the FP-Growth algorithm to extract high-confidence co-occurrence patterns among 19 codified conservation measures. By encoding expert habitat assessments into binary transactions, the analysis revealed 44 robust association rules, highlighting interdependent management actions that collectively improve species resilience and habitat conditions. These results provide actionable insights for integrated, evidence-based conservation planning. The approach demonstrates the interpretability, scalability, and practical relevance of ARM in biodiversity management, offering a replicable method for supporting adaptive ecological decision making across complex protected area networks.

26 June 2025 in Applied Sciences

Journal Article

Detecting Encrypted and Unencrypted Network Data Using Entropy Analysis and Confidence Intervals

The development of information security mechanisms follows a cyclic refinement model: new cryptographic solutions are proposed, their limitations are studied, and improvements are introduced to overcome those limitations. This paper contributes to this process by proposing a blind signature scheme with tunable anonymity, adapted to application-specific requirements. The core of the model is a parameter T that allows the user to balance confidentiality with transparency from the settings and adapt the signature behavior to the particular requirements of a beneficiary. Compared to the models currently used in blind signature protocols, this approach offers improved resistance to brute force attacks and improves security against adaptive and man-in-the-middle threats. Due to the reduced computational power requirements needed to calculate cryptographic primitives, it is usable for devices with power constraints. Being able to be integrated into a blockchain infrastructure, the process supports both persistent and verifiable data records, which gives it flexibility to adapt to different types of decentralized platforms.

22 February 2023 in Entropy

Journal Article

Teodora Popa