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A tree-based machine learning methodology to automatically classify software vulnerabilities.

, , , , , and . CSR, page 312-317. IEEE, (2021)

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A tree-based machine learning methodology to automatically classify software vulnerabilities., , , , , and . CSR, page 312-317. IEEE, (2021)Associating the Severity of Vulnerabilities with their Description., , and . CAiSE Workshops, volume 249 of Lecture Notes in Business Information Processing, page 231-242. Springer, (2016)WIVSS: a new methodology for scoring information systems vulnerabilities., , and . Panhellenic Conference on Informatics, page 83-90. ACM, (2013)Assessment of Vulnerability Severity using Text Mining., , and . PCI, page 49:1-49:6. ACM, (2017)Combining Statistical and Machine Learning Techniques in IoT Anomaly Detection for Smart Homes., , , and . CAMAD, page 1-6. IEEE, (2019)Μέθοδοι στατιστικής και μηχανικής μάθησης στην ασφάλεια πληροφοριών. Aristotle University Of Thessaloniki, Greece, (2019)National Archive of PhD Theses: oai:10442/45101.Combining text analysis techniques with unsupervised machine learning methodologies for improved software vulnerability management., , , , and . CSR, page 273-278. IEEE, (2022)A RAkEL-based methodology to estimate software vulnerability characteristics & score - an application to EU project ECHO., , , , , , and . Multim. Tools Appl., 81 (7): 9459-9479 (2022)The impact of information security events to the stock market: A systematic literature review., and . Comput. Secur., (2016)Estimated Time of Arrival in Autonomous Vehicles Using Gradient Boosting: Real-life case study in public transportation., , , , and . ISC2, page 1-7. IEEE, (2022)