@inproceedings{MTMT:34795721,
	title = {Swarmchestrate: Towards a Fully Decentralised Framework for Orchestrating Applications in the Cloud-to-Edge Continuum},
	url = {https://m2.mtmt.hu/api/publication/34795721},
	author = {Kiss, T and Ullah, A and Terstyanszky, G and Kao, O and Becker, S and Verginadis, Y and Michalas, A and Stankovski, V and Kertész, Attila and Ricci, E and Altmann, J and Egger, B and Tusa, F and Kovács, József and Lovas, Róbert},
	booktitle = {Advanced Information Networking and Applications},
	doi = {10.1007/978-3-031-57931-8_9},
	unique-id = {34795721},
	year = {2024},
	pages = {89-100},
	orcid-numbers = {Kertész, Attila/0000-0002-9457-2928; Kovács, József/0000-0002-7293-3016; Lovas, Róbert/0000-0001-9409-2855}
}

@inproceedings{MTMT:34795664,
	title = {Towards a Simulation as a Service Platform for the Cloud-to-Things Continuum},
	url = {https://m2.mtmt.hu/api/publication/34795664},
	author = {Wilson, Valdez and Baniata, Hamza and Márkus, András and Kertész, Attila},
	booktitle = {Euro-Par 2023: Parallel Processing Workshops},
	doi = {10.1007/978-3-031-48803-0_6},
	unique-id = {34795664},
	year = {2024},
	pages = {65-75},
	orcid-numbers = {Baniata, Hamza/0000-0003-1978-3175; Kertész, Attila/0000-0002-9457-2928}
}

@article{MTMT:34753193,
	title = {"Approaches to sentiment analysis of Hungarian political news at the sentence level"},
	url = {https://m2.mtmt.hu/api/publication/34753193},
	author = {Ring, Orsolya and Szabó, Martina Katalin and Guba, Csenge and Váradi, Bendegúz and Üveges, István},
	doi = {10.1007/s10579-023-09717-5},
	journal-iso = {LANG RESOUR EVAL},
	journal = {LANGUAGE RESOURCES AND EVALUATION},
	unique-id = {34753193},
	issn = {1574-020X},
	abstract = {Automated sentiment analysis of textual data is one of the central and most challenging tasks in political communication studies. However, the toolkits available are primarily for English texts and require contextual adaptation to produce valid results—especially concerning morphologically rich languages such as Hungarian. This study introduces (1) a new sentiment and emotion annotation framework that uses inductive approaches to identify emotions in the corpus and aggregate these emotions into positive, negative, and mixed sentiment categories, (2) a manually annotated sentiment data set with 5700 political news sentences, (3) a new Hungarian sentiment dictionary for political text analysis created via word embeddings, whose performance was compared with other available sentiment dictionaries. (4) Because of the limitations of sentiment analysis using dictionaries we have also applied various machine learning algorithms to analyze our dataset, (5) Last but not least to move towards state-of-the-art approaches, we have fine-tuned the Hungarian BERT-base model for sentiment analysis. Meanwhile, we have also tested how different pre-processing steps could affect the performance of machine-learning algorithms in the case of Hungarian texts.},
	year = {2024},
	eissn = {1574-0218},
	orcid-numbers = {Ring, Orsolya/0000-0002-3710-1118; Szabó, Martina Katalin/0000-0002-4192-4352; Üveges, István/0000-0001-5897-9379}
}

@article{MTMT:34499877,
	title = {Known Vulnerabilities of Open Source Projects: Where Are the Fixes?},
	url = {https://m2.mtmt.hu/api/publication/34499877},
	author = {Sabetta, Antonino and Ponta, Serena Elisa and Lozoya, Rocio Cabrera and Bezzi, Michele and Sacchetti, Tommaso and Greco, Matteo and Balogh, Gergő and Hegedűs, Péter and Ferenc, Rudolf and Paramitha, Ranindya and Pashchenko, Ivan and Papotti, Aurora and Milánkovich, Ákos and Massacci, Fabio},
	doi = {10.1109/MSEC.2023.3343836},
	journal-iso = {IEEE SECUR PRIV},
	journal = {IEEE SECURITY & PRIVACY},
	unique-id = {34499877},
	issn = {1540-7993},
	year = {2024},
	eissn = {1558-4046},
	orcid-numbers = {Sabetta, Antonino/0000-0003-3506-8374; Ponta, Serena Elisa/0000-0002-6208-4743; Lozoya, Rocio Cabrera/0000-0001-8911-7392; Bezzi, Michele/0000-0003-2084-0675; Balogh, Gergő/0000-0002-6781-5453; Hegedűs, Péter/0000-0003-4592-6504; Ferenc, Rudolf/0000-0001-8897-7403; Paramitha, Ranindya/0000-0002-6682-4243; Pashchenko, Ivan/0000-0001-8202-576X; Papotti, Aurora/0000-0003-3207-7662; Massacci, Fabio/0000-0002-1091-8486}
}

@article{MTMT:34431216,
	title = {Multilingual Analysis and Visualization of Bibliographic Metadata and Texts with the AVOBMAT Research Tool},
	url = {https://m2.mtmt.hu/api/publication/34431216},
	author = {Péter, Róbert and Szántó, Zsolt and Biacsi, Zoltán and Berend, Gábor and Bilicki, Vilmos},
	doi = {10.5334/johd.175},
	journal = {Journal of Open Humanities Data},
	volume = {10},
	unique-id = {34431216},
	year = {2024},
	eissn = {2059-481X},
	orcid-numbers = {Berend, Gábor/0000-0002-3845-4978}
}

@{MTMT:34532466,
	title = {Milyen támadások fenyegetik az egészségügyi adatok bizalmasságát?},
	url = {https://m2.mtmt.hu/api/publication/34532466},
	author = {Alexin, Zoltán},
	booktitle = {I. Alverad-Bánki Nemzetközi Kiberbiztonsági Konferencia},
	unique-id = {34532466},
	year = {2023},
	pages = {56-56},
	orcid-numbers = {Alexin, Zoltán/0000-0003-0616-2169}
}

@{MTMT:34532434,
	title = {What sorts of attacks are threatening the confidentiality of medical data?},
	url = {https://m2.mtmt.hu/api/publication/34532434},
	author = {Alexin, Zoltán},
	booktitle = {I. Alverad-Bánki Nemzetközi Kiberbiztonsági Konferencia},
	unique-id = {34532434},
	year = {2023},
	pages = {57-57},
	orcid-numbers = {Alexin, Zoltán/0000-0003-0616-2169}
}

@article{MTMT:34448268,
	title = {CrySyS dataset of CAN traffic logs containing fabrication and masquerade attacks},
	url = {https://m2.mtmt.hu/api/publication/34448268},
	author = {Gazdag, András Gábor and Ferenc, Rudolf and Buttyán, Levente},
	doi = {10.1038/s41597-023-02716-9},
	journal-iso = {SCI DATA},
	journal = {SCIENTIFIC DATA},
	volume = {10},
	unique-id = {34448268},
	abstract = {Despite their known security shortcomings, Controller Area Networks are widely used in modern vehicles. Research in the field has already proposed several solutions to increase the security of CAN networks, such as using anomaly detection methods to identify attacks. Modern anomaly detection procedures typically use machine learning solutions that require a large amount of data to be trained. This paper presents a novel CAN dataset specifically collected and generated to support the development of machine learning based anomaly detection systems. Our dataset contains 26 recordings of benign network traffic, amounting to more than 2.5 hours of traffic. We performed two types of attack on the benign data to create an attacked dataset representing most of the attacks previously proposed in the academic literature. As a novelty, we performed all attacks in two versions, modifying either one or two signals simultaneously. Along with the raw data, we also publish the source code used to generate the attacks to allow easy customization and extension of the dataset. © 2023, The Author(s).},
	year = {2023},
	eissn = {2052-4463},
	orcid-numbers = {Gazdag, András Gábor/0000-0002-4481-3308; Ferenc, Rudolf/0000-0001-8897-7403}
}

@article{MTMT:34219961,
	title = {A New Approach to Web Application Security: Utilizing GPT Language Models for Source Code Inspection},
	url = {https://m2.mtmt.hu/api/publication/34219961},
	author = {Szabó, Zoltán and Bilicki, Vilmos},
	doi = {10.3390/fi15100326},
	journal-iso = {FUTURE INTERNET},
	journal = {FUTURE INTERNET},
	volume = {15},
	unique-id = {34219961},
	abstract = {Due to the proliferation of large language models (LLMs) and their widespread use in applications such as ChatGPT, there has been a significant increase in interest in AI over the past year. Multiple researchers have raised the question: how will AI be applied and in what areas? Programming, including the generation, interpretation, analysis, and documentation of static program code based on promptsis one of the most promising fields. With the GPT API, we have explored a new aspect of this: static analysis of the source code of front-end applications at the endpoints of the data path. Our focus was the detection of the CWE-653 vulnerability—inadequately isolated sensitive code segments that could lead to unauthorized access or data leakage. This type of vulnerability detection consists of the detection of code segments dealing with sensitive data and the categorization of the isolation and protection levels of those segments that were previously not feasible without human intervention. However, we believed that the interpretive capabilities of GPT models could be explored to create a set of prompts to detect these cases on a file-by-file basis for the applications under study, and the efficiency of the method could pave the way for additional analysis tasks that were previously unavailable for automation. In the introduction to our paper, we characterize in detail the problem space of vulnerability and weakness detection, the challenges of the domain, and the advances that have been achieved in similarly complex areas using GPT or other LLMs. Then, we present our methodology, which includes our classification of sensitive data and protection levels. This is followed by the process of preprocessing, analyzing, and evaluating static code. This was achieved through a series of GPT prompts containing parts of static source code, utilizing few-shot examples and chain-of-thought techniques that detected sensitive code segments and mapped the complex code base into manageable JSON structures.Finally, we present our findings and evaluation of the open source project analysis, comparing the results of the GPT-based pipelines with manual evaluations, highlighting that the field yields a high research value. The results show a vulnerability detection rate for this particular type of model of 88.76%, among others.},
	year = {2023},
	eissn = {1999-5903},
	orcid-numbers = {Szabó, Zoltán/0000-0003-3863-7595}
}

@article{MTMT:34167552,
	title = {Distributed scalability tuning for evolutionary sharding optimization with Random-equivalent security in permissionless Blockchain},
	url = {https://m2.mtmt.hu/api/publication/34167552},
	author = {Baniata, Hamza and Anaqreh, Ahmad and Kertész, Attila},
	doi = {10.1016/j.iot.2023.100955},
	journal-iso = {INTERNET THINGS-NETH},
	journal = {INTERNET OF THINGS},
	volume = {24},
	unique-id = {34167552},
	issn = {2543-1536},
	year = {2023},
	eissn = {2542-6605},
	orcid-numbers = {Baniata, Hamza/0000-0003-1978-3175; Anaqreh, Ahmad/0000-0002-3971-2684; Kertész, Attila/0000-0002-9457-2928}
}