Výpis publikací

MACÁK, Martin a Martin ŠEPEĽA. Discord Achievement System for Education Gamification. Online. In 16th International Conference on Education and New Learning Technologies. 2024.

OŠLEJŠEK, Radek, Martin MACÁK a Karolína DOČKALOVÁ BURSKÁ. Hands-on Cybersecurity Training Behavior Data for Process Mining. Data in Brief. Elsevier, 2024, roč. 52, February 2024, s. 1-12. ISSN 2352-3409. Dostupné z: https://dx.doi.org/10.1016/j.dib.2023.109956.

DAUBNER, Lukáš, Martin MACÁK, Raimundas MATULEVIČIUS, Barbora BÜHNOVÁ, Sofija MAKSOVIĆ a Tomáš PITNER. Addressing insider attacks via forensic-ready risk management. JOURNAL OF INFORMATION SECURITY AND APPLICATIONS. ENGLAND: ELSEVIER, 2023, roč. 73, March 2023, s. 103433-103449. ISSN 2214-2126. Dostupné z: https://dx.doi.org/10.1016/j.jisa.2023.103433.

MACÁK, Martin, Tomáš REBOK, Matúš ŠTOVČIK, Mouzhi GE, Bruno ROSSI a Barbora BÜHNOVÁ. CopAS: A Big Data Forensic Analytics System. Online. In Proceedings of the 8th International Conference on Internet of Things, Big Data and Security IoTBDS - Volume 1. Setubal, Portugal: SciTePress, 2023, s. 150-161. ISBN 978-989-758-643-9. Dostupné z: https://dx.doi.org/10.5220/0011929000003482.

MACÁK, Martin, Radek OŠLEJŠEK a Barbora BÜHNOVÁ. Detecting Masquerading Traitors from Process Visualization of Computer. Online. In 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). Neuveden: IEEE, 2023, s. 1935-1940. ISBN 979-8-3503-8200-6. Dostupné z: https://dx.doi.org/10.1109/TrustCom60117.2023.00263.

KRATOCHVÍL, Tomáš, Martin VACULÍK a Martin MACÁK. Gamification tailored for novelty effect in distance learning during COVID-19. Frontiers in Education. Lausanne: Frontiers Media, 2023, roč. 8, February, s. 1-11. ISSN 2504-284X. Dostupné z: https://dx.doi.org/10.3389/feduc.2023.1051227.

MACÁK, Martin, Radek OŠLEJŠEK a Barbora BÜHNOVÁ. Applying Process Discovery to Cybersecurity Training: An Experience Report. Online. In 2022 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW). Neuveden: IEEE, 2022, s. 394-402. ISBN 978-1-6654-9560-8. Dostupné z: https://dx.doi.org/10.1109/EuroSPW55150.2022.00047.

MACÁK, Martin, Lukáš DAUBNER, Mohammadreza FANI SANI a Barbora BÜHNOVÁ. Cybersecurity Analysis via Process Mining: A Systematic Literature Review. Online. In Advanced Data Mining and Applications. Cham, Switzerland: Springer, 2022, s. 393-407. ISBN 978-3-030-95404-8. Dostupné z: https://dx.doi.org/10.1007/978-3-030-95405-5_28.

CHREN, Stanislav, Martin MACÁK, Bruno ROSSI a Barbora BÜHNOVÁ. Evaluating Code Improvements in Software Quality Course Projects. Online. In Proceedings of The 25th International Conference on Evaluation and Assessment in Software Engineering. New York, NY, USA: Association for Computing Machinery (ACM), 2022, s. 160-169. ISBN 978-1-4503-9613-4. Dostupné z: https://dx.doi.org/10.1145/3530019.3530036.

MACÁK, Martin, Lukáš DAUBNER, Júlia JAMNICKÁ a Barbora BÜHNOVÁ. Game Achievement Analysis: Process Mining Approach. Online. In Advanced Data Mining and Applications. Cham: Springer International Publishing, 2022, s. 68-82. ISBN 978-3-030-95407-9. Dostupné z: https://dx.doi.org/10.1007/978-3-030-95408-6_6.

MACÁK, Martin, Radek OŠLEJŠEK a Barbora BÜHNOVÁ. Process Mining Analysis of Puzzle-Based Cybersecurity Training. Online. In Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1 (ITiCSE '22). New York, NY, USA: Association for Computing Machinery, 2022, s. 449-455. ISBN 978-1-4503-9201-3. Dostupné z: https://dx.doi.org/10.1145/3502718.3524819.

MACÁK, Martin, Lukáš DAUBNER, Mohammadreza FANI SANI a Barbora BÜHNOVÁ. Process Mining Usage in Cybersecurity and Software Reliability Analysis: A Systematic Literature Review. Array. Elsevier Inc., 2022, roč. 13, č. 1, s. 1-14. ISSN 2590-0056. Dostupné z: https://dx.doi.org/10.1016/j.array.2021.100120.

MACÁK, Martin, Radek VÁCLAVEK, Daša KUŠNIRÁKOVÁ, Matulevičius RAIMUNDAS a Barbora BÜHNOVÁ. Scenarios for Process-Aware Insider Attack Detection in Manufacturing. Online. In Proceedings of the 17th International Conference on Availability, Reliability and Security. New York, NY, USA: Association for Computing Machinery, 2022, s. 860-869. ISBN 978-1-4503-9670-7. Dostupné z: https://dx.doi.org/10.1145/3538969.3544449.

MACÁK, Martin, Štefan BOJNÁK a Barbora BÜHNOVÁ. Identification of Unintentional Perpetrator Attack Vectors using Simulation Game: A Case Study. Online. In Proceedings of the 16th Conference on Computer Science and Intelligence Systems. New York: IEEE, 2021, s. 349-356. ISBN 978-83-959183-8-4. Dostupné z: https://dx.doi.org/10.15439/2021F85.

MACÁK, Martin, Daniela KRÚŽELOVÁ, Stanislav CHREN a Barbora BÜHNOVÁ. Using Process Mining for Git Log Analysis of Projects in a Software Development Course. Education and Information Technologies. 2021, roč. 26, č. 5, s. 5939-5969. ISSN 1360-2357. Dostupné z: https://dx.doi.org/10.1007/s10639-021-10564-6.

MACÁK, Martin, Mouzhi GE a Barbora BÜHNOVÁ. A Cross-domain Comparative Study of Big Data Architectures. International Journal of Cooperative Information Systems. 2020, roč. 29, č. 4, s. 1-27. ISSN 0218-8430. Dostupné z: https://dx.doi.org/10.1142/S0218843020300016.

REBOK, Tomáš, Michal BATKO, Milan ČERMÁK, Martin DRAŠAR, Daniel TOVARŇÁK, Pavel ZEZULA, Martin MACÁK, Matúš GUOTH, Matej BABEJ, Dávid BRILLA a Martin KAŽIMÍR. ANALYZA – Datový sklad. 2020.

MACÁK, Martin, Hind BANGUI, Barbora BÜHNOVÁ, András J. MOLNÁR a Csaba István SIDLÓ. Big Data Processing Tools Navigation Diagram. Online. In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS. Setubal, Portugal: SciTePress, 2020, s. 304-312. ISBN 978-989-758-426-8. Dostupné z: https://dx.doi.org/10.5220/0009406403040312.

MACÁK, Martin, Matúš ŠTOVČIK, Barbora BÜHNOVÁ a Michal MERJAVÝ. How well a multi-model database performs against its single-model variants: Benchmarking OrientDB with Neo4j and MongoDB. Online. In Proceedings of the 2020 Federated Conference on Computer Science and Information Systems. New York: IEEE, 2020, s. 463-470. ISBN 978-83-955416-7-4. Dostupné z: https://dx.doi.org/10.15439/2020F76.

MACÁK, Martin, Agáta KRUŽÍKOVÁ, Lukáš DAUBNER a Barbora BÜHNOVÁ. Simulation Games Platform for Unintentional Perpetrator Attack Vector Identification. Online. In ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops. New York, NY, USA: Association for Computing Machinery, 2020, s. 222-229. ISBN 978-1-4503-7963-2. Dostupné z: https://dx.doi.org/10.1145/3387940.3391475.

MACÁK, Martin, Matúš ŠTOVČIK a Barbora BÜHNOVÁ. The Suitability of Graph Databases for Big Data Analysis: A Benchmark. Online. In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS. Neuveden: SciTePress, 2020, s. 213-220. ISBN 978-989-758-426-8. Dostupné z: https://dx.doi.org/10.5220/0009350902130220.

MACÁK, Martin, Ivan VANÁT, Michal MERJAVÝ, Tomáš JEVOČIN a Barbora BÜHNOVÁ. Towards Process Mining Utilization in Insider Threat Detection from Audit Logs. Online. In 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS). New York: IEEE, 2020, s. 250-255. ISBN 978-0-7381-1180-3. Dostupné z: https://dx.doi.org/10.1109/SNAMS52053.2020.9336573.

DAUBNER, Lukáš, Martin MACÁK, Barbora BÜHNOVÁ a Tomáš PITNER. Towards verifiable evidence generation in forensic-ready systems. Online. In 2020 IEEE International Conference on Big Data (Big Data). Atlanta, United States: IEEE, 2020, s. 2264-2269. ISBN 978-1-7281-6251-5. Dostupné z: https://dx.doi.org/10.1109/BigData50022.2020.9378035.

DAUBNER, Lukáš, Martin MACÁK, Barbora BÜHNOVÁ a Tomáš PITNER. Verification of Forensic Readiness in Software Development: A Roadmap. Online. In Proceedings of the 35th Annual ACM Symposium on Applied Computing. New York, NY, USA: Association for Computing Machinery, 2020, s. 1658-1661. ISBN 978-1-4503-6866-7. Dostupné z: https://dx.doi.org/10.1145/3341105.3374094.

LIPČÁK, Peter, Martin MACÁK a Bruno ROSSI. Big Data Platform for Smart Grids Power Consumption Anomaly Detection. Online. In Proceedings of the 2019 Federated Conference on Computer Science and Information Systems. New York: IEEE, 2019, s. 771-780. ISBN 978-1-5386-8005-6. Dostupné z: https://dx.doi.org/10.15439/2019F210.

CHREN, Stanislav, Barbora BÜHNOVÁ, Martin MACÁK, Lukáš DAUBNER a Bruno ROSSI. Mistakes in UML Diagrams: Analysis of Student Projects in a Software Engineering Course. Online. In Sarah Beecham, Daniela Damian. Proceedings of the 41st International Conference on Software Engineering: Software Engineering Education and Training. Piscataway, NJ, USA: IEEE Press, 2019, s. 100-109. ISBN 978-1-7281-1000-4. Dostupné z: https://dx.doi.org/10.1109/ICSE-SEET.2019.00019.