| dc.contributor.author | Pérez-Jove, Rubén | |
| dc.contributor.author | Munteanu, Cristian-Robert | |
| dc.contributor.author | Pazos, A. | |
| dc.contributor.author | Vázquez-Naya, José | |
| dc.date.accessioned | 2022-01-05T11:14:35Z | |
| dc.date.available | 2022-01-05T11:14:35Z | |
| dc.date.issued | 2021 | |
| dc.identifier.citation | Pérez-Jove, R.; Munteanu, C.R.; Sierra, A.P.; Vázquez-Naya, J.M. Applying Artificial Intelligence for Operating System Fingerprinting. Eng. Proc. 2021, 7, 51. https://doi.org/10.3390/engproc2021007051 | es_ES |
| dc.identifier.uri | http://hdl.handle.net/2183/29311 | |
| dc.description | Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021. | es_ES |
| dc.description.abstract | [Abstract] In the field of computer security, the possibility of knowing which specific version of an operating system is running behind a machine can be useful, to assist in a penetration test or monitor the devices connected to a specific network. One of the most widespread tools that better provides this functionality is Nmap, which follows a rule-based approach for this process. In this context, applying machine learning techniques seems to be a good option for addressing this task. The present work explores the strengths of different machine learning algorithms to perform operating system fingerprinting, using for that, the Nmap reference database. Moreover, some optimizations were applied to the method which brought the best results, random forest, obtaining an accuracy higher than 96%. | es_ES |
| dc.description.sponsorship | CITIC, as a research center accredited by the Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported—80% through ERDF, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaría Xeral de Universidades (Grant ED431G 2019/01). This project was also supported by the “Consellería de Cultura, Educación e Ordenación Universitaria” via the Consolidation and Structuring of Competitive Research Units–Competitive Reference Groups (ED431C 2018/49) and the COST Action 17124 DigForAsp, supported by COST (European Cooperation in Science and Technology, www.cost.eu, (accessed on 25 October 2021)). | es_ES |
| dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
| dc.description.sponsorship | Xunta de Galicia; ED431C 2018/49 | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | MDPI | es_ES |
| dc.relation.uri | https://doi.org/10.3390/engproc2021007051 | es_ES |
| dc.rights | Atribución 3.0 España | es_ES |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
| dc.subject | Operating systems | es_ES |
| dc.subject | Fingerprinting | es_ES |
| dc.subject | Nmap | es_ES |
| dc.subject | Machine learning | es_ES |
| dc.title | Applying Artificial Intelligence for Operating System Fingerprinting | es_ES |
| dc.type | conference output | es_ES |
| dc.rights.accessRights | open access | es_ES |
| UDC.journalTitle | Engineering Proceedings | es_ES |
| UDC.volume | 7 | es_ES |
| UDC.issue | 1 | es_ES |
| UDC.startPage | 51 | es_ES |
| dc.identifier.doi | 10.3390/engproc2021007051 | |
| UDC.coleccion | Investigación | es_ES |
| UDC.departamento | Ciencias da Computación e Tecnoloxías da Información | es_ES |
| UDC.grupoInv | Redes de Neuronas Artificiais e Sistemas Adaptativos -Informática Médica e Diagnóstico Radiolóxico (RNASA - IMEDIR) | es_ES |
