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| Camp DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.contributor.author | Венгерський, Петро | |
| dc.contributor.author | Лесик, Володимир | |
| dc.coverage.temporal | 27-29 травня 2026 року | |
| dc.coverage.temporal | 27-29 May 2026 | |
| dc.date.accessioned | 2026-08-27T15:43:54Z | - |
| dc.date.available | 2026-08-27T15:43:54Z | - |
| dc.date.created | 2026-05-27 | |
| dc.date.issued | 2026-05-27 | |
| dc.identifier.citation | Венгерський П. Еволюція методів виявлення шкідливих URL: від евристик до трансформерних архітектур / Петро Венгерський, Володимир Лесик // ITSEC, 27-29 травня 2026 року. — Т. : ТНТУ, 2026. — С. 227–229. | |
| dc.identifier.uri | http://elartu.tntu.edu.ua/handle/lib/53781 | - |
| dc.format.extent | 227-229 | |
| dc.language.iso | uk | |
| dc.publisher | ТНТУ | |
| dc.publisher | TNTU | |
| dc.relation.ispartof | Матеріали ⅩⅤ Міжнародної науково-технічної конференції ITSec: Безпека інформаційних технологій, 2026 | |
| dc.relation.ispartof | Proceedings of the 15th International Scientific and Technical Conference ITSec: Information Technology Security, 2026 | |
| dc.title | Еволюція методів виявлення шкідливих URL: від евристик до трансформерних архітектур | |
| dc.type | Conference Abstract | |
| dc.rights.holder | © Кафедра кібербезпеки Тернопільського національного технічного університету імені Івана Пулюя, 2026 | |
| dc.coverage.placename | Тернопіль | |
| dc.coverage.placename | Ternopil | |
| dc.format.pages | 3 | |
| dc.subject.udc | 519.6 2 | |
| dc.relation.references | 1. Sahoo D., Liu C., Hoi S.C.H. Malicious URL Detection using Machine Learning: A Survey. arXiv preprint. – 2017. – arXiv:1701.07179. – doi:10.48550/arXiv.1701.07179. | |
| dc.relation.references | 2. Aljabri M., Altamimi H., Albelali S., Al-Harbi M., Alhuraib H., Alotaibi N., Alahmadi A., Alhaidari F., Mohammad R., Salah K. Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions. IEEE Access. – 2022. – doi:10.1109/ACCESS.2022.3222307. | |
| dc.relation.references | 3. Tian Y., Yu Y., Sun J., Wang Y. From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories. arXiv preprint. – 2025. – arXiv:2504.16449. – doi:10.48550/arXiv.2504.16449. | |
| dc.relation.references | 4. Dhotre A. Malicious URLs Detection using Lexical Features based on Machine Learning. IJSRD. – 2023. – Vol. 11, Issue 8. | |
| dc.relation.references | 5. Joshi A., Lloyd L., Westin P., Seethapathy S. Using Lexical Features for Malicious URL Detection – A Machine Learning Approach. arXiv preprint. – 2019. – arXiv:1910.06277. – doi:10.48550/arXiv.1910.06277. | |
| dc.relation.references | 6. Hamadouche S., Boudraa O., Gasmi M. Combining Lexical, Host, and Content-based Features for Phishing Websites Detection using Machine Learning Models. EAI Endorsed Transactions on Scalable Information Systems. – 2024. – Vol. 11, no. 6. – doi:10.4108/eetsis.4421. | |
| dc.relation.references | 7. Do N., Selamat A., Krejcar O., Herrera-Viedma E., Fujita H. Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions. IEEE Access. – 2022. – Vol. 10. – P. 36543–36564. – doi:10.1109/ACCESS.2022.3151903. | |
| dc.relation.references | 8. Turk F., Kilicaslan M. Malicious URL Detection with Advanced Machine Learning and Optimization-Supported Deep Learning Models. Appl. Sci. – 2025. – Vol. 15, no. 18. – Art. 10090. – doi:10.3390/app151810090. | |
| dc.relation.references | 9. Kibriya H., Amin R., Alshamrani S.S. et al. Lightweight Malicious URL Detection using Deep Learning and Large Language Models. Sci. Rep. – 2025. – Vol. 15. – Art. 43044. – doi:10.1038/s41598-025-26653-2. | |
| dc.relation.references | 10. Elsadig M., Ibrahim A.O., Basheer S., Alohali M.A., Alshunaifi S., Alqahtani H., Alharbi N., Nagmeldin W. Intelligent Deep Machine Learning Cyber Phishing URL Detection Based on BERT Features Extraction. Electronics. – 2022. – Vol. 11, no. 22. – Art. 3647. – doi:10.3390/electronics11223647. | |
| dc.relation.references | 11. Kumi S., Lim C., Lee S.-G. Malicious URL Detection Based on Associative Classification. Entropy. – 2021. – Vol. 23, no. 2. – Art. 182. – doi:10.3390/e23020182. | |
| dc.relation.references | 12. Jeeva S.C., Rajsingh E.B. Intelligent Phishing URL Detection using Association Rule Mining. Hum. Cent. Comput. Inf. Sci. – 2016. – Vol. 6. – Art. 10. – doi:10.1186/s13673-016-0064-3. | |
| dc.relation.references | 13. Alsaedi M., Ghaleb F.A., Saeed F., Ahmad J., Alasli M. Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning. Sensors. – 2022. – Vol. 22, no. 9. – Art. 3373. – doi:10.3390/s22093373. | |
| dc.relation.references | 14. Rafsanjani A.S., Kamaruddin N.B., Behjati M., Aslam S., Sarfaraz A., Amphawan A. Enhancing Malicious URL Detection: A Novel Framework Leveraging Priority Coefficient and Feature Evaluation. IEEE Access. – 2024. – Vol. 12. – P. 85001–85026. – doi:10.1109/ACCESS.2024.3412331. | |
| dc.relation.referencesen | 1. Sahoo D., Liu C., Hoi S.C.H. Malicious URL Detection using Machine Learning: A Survey. arXiv preprint, 2017, arXiv:1701.07179, doi:10.48550/arXiv.1701.07179. | |
| dc.relation.referencesen | 2. Aljabri M., Altamimi H., Albelali S., Al-Harbi M., Alhuraib H., Alotaibi N., Alahmadi A., Alhaidari F., Mohammad R., Salah K. Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions. IEEE Access, 2022, doi:10.1109/ACCESS.2022.3222307. | |
| dc.relation.referencesen | 3. Tian Y., Yu Y., Sun J., Wang Y. From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories. arXiv preprint, 2025, arXiv:2504.16449, doi:10.48550/arXiv.2504.16449. | |
| dc.relation.referencesen | 4. Dhotre A. Malicious URLs Detection using Lexical Features based on Machine Learning. IJSRD, 2023, Vol. 11, Issue 8. | |
| dc.relation.referencesen | 5. Joshi A., Lloyd L., Westin P., Seethapathy S. Using Lexical Features for Malicious URL Detection – A Machine Learning Approach. arXiv preprint, 2019, arXiv:1910.06277, doi:10.48550/arXiv.1910.06277. | |
| dc.relation.referencesen | 6. Hamadouche S., Boudraa O., Gasmi M. Combining Lexical, Host, and Content-based Features for Phishing Websites Detection using Machine Learning Models. EAI Endorsed Transactions on Scalable Information Systems, 2024, Vol. 11, no. 6, doi:10.4108/eetsis.4421. | |
| dc.relation.referencesen | 7. Do N., Selamat A., Krejcar O., Herrera-Viedma E., Fujita H. Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions. IEEE Access, 2022, Vol. 10, P. 36543–36564, doi:10.1109/ACCESS.2022.3151903. | |
| dc.relation.referencesen | 8. Turk F., Kilicaslan M. Malicious URL Detection with Advanced Machine Learning and Optimization-Supported Deep Learning Models. Appl. Sci, 2025, Vol. 15, no. 18, Art. 10090, doi:10.3390/app151810090. | |
| dc.relation.referencesen | 9. Kibriya H., Amin R., Alshamrani S.S. et al. Lightweight Malicious URL Detection using Deep Learning and Large Language Models. Sci. Rep, 2025, Vol. 15, Art. 43044, doi:10.1038/s41598-025-26653-2. | |
| dc.relation.referencesen | 10. Elsadig M., Ibrahim A.O., Basheer S., Alohali M.A., Alshunaifi S., Alqahtani H., Alharbi N., Nagmeldin W. Intelligent Deep Machine Learning Cyber Phishing URL Detection Based on BERT Features Extraction. Electronics, 2022, Vol. 11, no. 22, Art. 3647, doi:10.3390/electronics11223647. | |
| dc.relation.referencesen | 11. Kumi S., Lim C., Lee S.-G. Malicious URL Detection Based on Associative Classification. Entropy, 2021, Vol. 23, no. 2, Art. 182, doi:10.3390/e23020182. | |
| dc.relation.referencesen | 12. Jeeva S.C., Rajsingh E.B. Intelligent Phishing URL Detection using Association Rule Mining. Hum. Cent. Comput. Inf. Sci, 2016, Vol. 6, Art. 10, doi:10.1186/s13673-016-0064-3. | |
| dc.relation.referencesen | 13. Alsaedi M., Ghaleb F.A., Saeed F., Ahmad J., Alasli M. Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning. Sensors, 2022, Vol. 22, no. 9, Art. 3373, doi:10.3390/s22093373. | |
| dc.relation.referencesen | 14. Rafsanjani A.S., Kamaruddin N.B., Behjati M., Aslam S., Sarfaraz A., Amphawan A. Enhancing Malicious URL Detection: A Novel Framework Leveraging Priority Coefficient and Feature Evaluation. IEEE Access, 2024, Vol. 12, P. 85001–85026, doi:10.1109/ACCESS.2024.3412331. | |
| dc.contributor.affiliation | Львівський національний університет імені Івана Франка | |
| dc.citation.journalTitle | Матеріали ⅩⅤ Міжнародної науково-технічної конференції ITSec: Безпека інформаційних технологій | |
| dc.citation.spage | 227 | |
| dc.citation.epage | 229 | |
| dc.citation.conference | ⅩⅤ Міжнародна науково-технічна конференція ITSec: Безпека інформаційних технологій | |
| dc.identifier.citation2015 | Венгерський П., Лесик В. Еволюція методів виявлення шкідливих URL: від евристик до трансформерних архітектур // ITSEC, Тернопіль, 27-29 травня 2026 року. 2026. С. 227–229. | |
| dc.identifier.citationenAPA | Venherskii, P., & Lesik, V. (2026). Evoliutsiia metodiv vyiavlennia shkidlyvykh URL: vid evrystyk do transformernykh arkhitektur. Proceedings of the 15th International Scientific and Technical Conference ITSec: Information Technology Security, 27-29 May 2026, Ternopil, 227-229. TNTU. [in Ukrainian]. | |
| dc.identifier.citationenCHICAGO | Venherskii P., Lesik V. (2026) Evoliutsiia metodiv vyiavlennia shkidlyvykh URL: vid evrystyk do transformernykh arkhitektur. Proceedings of the 15th International Scientific and Technical Conference ITSec: Information Technology Security (Tern., 27-29 May 2026), pp. 227-229 [in Ukrainian]. | |
| Apareix a les col·leccions: | ⅩⅤ Міжнародна науково-технічна конференція ITSec: Безпека інформаційних технологій (2026) | |
Arxius per aquest ítem:
| Arxiu | Descripció | Mida | Format | |
|---|---|---|---|---|
| ITSEC_2026_Venherskii_P-Evoliutsiia_metodiv_227-229.pdf | 740,88 kB | Adobe PDF | Veure/Obrir | |
| ITSEC_2026_Venherskii_P-Evoliutsiia_metodiv_227-229__COVER.png | 938,05 kB | image/png | Veure/Obrir |
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