Applying Artificial Intelligence for Operating System Fingerprinting Article Swipe
Rubén Pérez-Jove
,
Cristian R. Munteanu
,
Alejandro Pazos
,
José M. Vázquez-Naya
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/engproc2021007051
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/engproc2021007051
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%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/engproc2021007051
- https://www.mdpi.com/2673-4591/7/1/51/pdf?version=1635320860
- OA Status
- gold
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3210228718
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3210228718Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/engproc2021007051Digital Object Identifier
- Title
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Applying Artificial Intelligence for Operating System FingerprintingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-10-26Full publication date if available
- Authors
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Rubén Pérez-Jove, Cristian R. Munteanu, Alejandro Pazos, José M. Vázquez-NayaList of authors in order
- Landing page
-
https://doi.org/10.3390/engproc2021007051Publisher landing page
- PDF URL
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https://www.mdpi.com/2673-4591/7/1/51/pdf?version=1635320860Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2673-4591/7/1/51/pdf?version=1635320860Direct OA link when available
- Concepts
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Computer science, Field (mathematics), Process (computing), Machine learning, Context (archaeology), Random forest, Artificial intelligence, Task (project management), Network security, Data mining, Operating system, Engineering, Systems engineering, Pure mathematics, Biology, Mathematics, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2023: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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