TY - CHAP AU - Kiss, T AU - Ullah, A AU - Terstyanszky, G AU - Kao, O AU - Becker, S AU - Verginadis, Y AU - Michalas, A AU - Stankovski, V AU - Kertész, Attila AU - Ricci, E AU - Altmann, J AU - Egger, B AU - Tusa, F AU - Kovács, József AU - Lovas, Róbert ED - Barolli, L TI - Swarmchestrate: Towards a Fully Decentralised Framework for Orchestrating Applications in the Cloud-to-Edge Continuum T2 - Advanced Information Networking and Applications PB - Springer Nature Switzerland CY - Cham SN - 9783031579318 T3 - Lecture Notes on Data Engineering and Communications Technologies, ISSN 2367-4512 ; 203. PY - 2024 SP - 89 EP - 100 PG - 12 DO - 10.1007/978-3-031-57931-8_9 UR - https://m2.mtmt.hu/api/publication/34795721 ID - 34795721 LA - English DB - MTMT ER - TY - CHAP AU - Wilson, Valdez AU - Baniata, Hamza AU - Márkus, András AU - Kertész, Attila ED - Zeinalipour, Demetris ED - Blanco Heras, Dora ED - Pallis, George ED - Herodotou, Herodotos ED - Trihinas, Demetris ED - Balouek, Daniel ED - Diehl, Patrick ED - Cojean, Terry ED - Fürlinger, Karl ED - Kirkeby, Maja Hanne ED - Nardellli, Matteo ED - Di Sanzo, Pierangelo TI - Towards a Simulation as a Service Platform for the Cloud-to-Things Continuum T2 - Euro-Par 2023: Parallel Processing Workshops PB - Springer Nature Switzerland CY - Cham SN - 9783031488030 T3 - Lecture Notes in Computer Science, ISSN 0302-9743 ; 14352. PY - 2024 SP - 65 EP - 75 PG - 11 DO - 10.1007/978-3-031-48803-0_6 UR - https://m2.mtmt.hu/api/publication/34795664 ID - 34795664 LA - English DB - MTMT ER - TY - JOUR AU - Ring, Orsolya AU - Szabó, Martina Katalin AU - Guba, Csenge AU - Váradi, Bendegúz AU - Üveges, István TI - "Approaches to sentiment analysis of Hungarian political news at the sentence level" JF - LANGUAGE RESOURCES AND EVALUATION J2 - LANG RESOUR EVAL PY - 2024 PG - 29 SN - 1574-020X DO - 10.1007/s10579-023-09717-5 UR - https://m2.mtmt.hu/api/publication/34753193 ID - 34753193 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Sabetta, Antonino AU - Ponta, Serena Elisa AU - Lozoya, Rocio Cabrera AU - Bezzi, Michele AU - Sacchetti, Tommaso AU - Greco, Matteo AU - Balogh, Gergő AU - Hegedűs, Péter AU - Ferenc, Rudolf AU - Paramitha, Ranindya AU - Pashchenko, Ivan AU - Papotti, Aurora AU - Milánkovich, Ákos AU - Massacci, Fabio TI - Known Vulnerabilities of Open Source Projects: Where Are the Fixes? JF - IEEE SECURITY & PRIVACY J2 - IEEE SECUR PRIV PY - 2024 PG - 11 SN - 1540-7993 DO - 10.1109/MSEC.2023.3343836 UR - https://m2.mtmt.hu/api/publication/34499877 ID - 34499877 LA - English DB - MTMT ER - TY - JOUR AU - Péter, Róbert AU - Szántó, Zsolt AU - Biacsi, Zoltán AU - Berend, Gábor AU - Bilicki, Vilmos TI - Multilingual Analysis and Visualization of Bibliographic Metadata and Texts with the AVOBMAT Research Tool JF - Journal of Open Humanities Data VL - 10 PY - 2024 PG - 10 SN - 2059-481X DO - 10.5334/johd.175 UR - https://m2.mtmt.hu/api/publication/34431216 ID - 34431216 LA - English DB - MTMT ER - TY - CHAP AU - Alexin, Zoltán ED - Répás, József TI - Milyen támadások fenyegetik az egészségügyi adatok bizalmasságát? T2 - I. Alverad-Bánki Nemzetközi Kiberbiztonsági Konferencia PB - Óbudai Egyetem CY - Budapest SN - 9789634493440 PY - 2023 SP - 56 EP - 56 PG - 1 UR - https://m2.mtmt.hu/api/publication/34532466 ID - 34532466 LA - Hungarian DB - MTMT ER - TY - CHAP AU - Alexin, Zoltán ED - Répás, József TI - What sorts of attacks are threatening the confidentiality of medical data? T2 - I. Alverad-Bánki Nemzetközi Kiberbiztonsági Konferencia PB - Óbudai Egyetem CY - Budapest SN - 9789634493440 PY - 2023 SP - 57 EP - 57 PG - 1 UR - https://m2.mtmt.hu/api/publication/34532434 ID - 34532434 LA - English DB - MTMT ER - TY - JOUR AU - Gazdag, András Gábor AU - Ferenc, Rudolf AU - Buttyán, Levente TI - CrySyS dataset of CAN traffic logs containing fabrication and masquerade attacks JF - SCIENTIFIC DATA J2 - SCI DATA VL - 10 PY - 2023 IS - 1 PG - 11 SN - 2052-4463 DO - 10.1038/s41597-023-02716-9 UR - https://m2.mtmt.hu/api/publication/34448268 ID - 34448268 N1 - CrySyS Lab, Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary Department of Software Engineering, University of Szeged, Szeged, Hungary Export Date: 22 December 2023 Correspondence Address: Gazdag, A.; CrySyS Lab, Hungary; email: andras.gazdag@crysys.hu Funding details: Mesterséges Intelligencia Nemzeti Laboratórium, MILAB, 138903 Funding details: European Commission, EC, RRF-2.3.1-21-2022-00004 Funding details: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA Funding text 1: This work has been supported by the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory and Project no. 138903 implemented with the support provided by the Ministry of Innovation and Technology from the National Research, Development, and Innovation Fund, financed under the FK_21 funding scheme. AB - 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). LA - English DB - MTMT ER - TY - JOUR AU - Szabó, Zoltán AU - Bilicki, Vilmos TI - A New Approach to Web Application Security: Utilizing GPT Language Models for Source Code Inspection JF - FUTURE INTERNET J2 - FUTURE INTERNET VL - 15 PY - 2023 IS - 10 PG - 27 SN - 1999-5903 DO - 10.3390/fi15100326 UR - https://m2.mtmt.hu/api/publication/34219961 ID - 34219961 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Baniata, Hamza AU - Anaqreh, Ahmad AU - Kertész, Attila TI - Distributed scalability tuning for evolutionary sharding optimization with Random-equivalent security in permissionless Blockchain JF - INTERNET OF THINGS J2 - INTERNET THINGS-NETH VL - 24 PY - 2023 PG - 17 SN - 2543-1536 DO - 10.1016/j.iot.2023.100955 UR - https://m2.mtmt.hu/api/publication/34167552 ID - 34167552 LA - English DB - MTMT ER -