Classifying computer session data using self-organizing maps

We propose an advanced solution to track persistent computer intruders inside a UNIX-based system by clustering sessions into groups bearing similar characteristics according to expertise and type of work. Our semi-supervised method based on Self-Organizing Map (SOM) accomplishes classification of f...

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Detalles Bibliográficos
Autores principales: Estrada, V.C., Nakao, A., Segura, E.C.
Formato: CONF
Materias:
Computer scientists
Computer sessions
Keystroke patterns
Novice programmer
Semi-supervised method
Artificial intelligence
Biometrics
Conformal mapping
Self organizing maps
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_97807695_v1_n_p48_Estrada
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_97807695_v1_n_p48_Estrada
record_format dspace
spelling todo:paper_97807695_v1_n_p48_Estrada2023-10-03T16:42:48Z Classifying computer session data using self-organizing maps Estrada, V.C. Nakao, A. Segura, E.C. Computer scientists Computer sessions Keystroke patterns Novice programmer Semi-supervised method Artificial intelligence Biometrics Conformal mapping Self organizing maps We propose an advanced solution to track persistent computer intruders inside a UNIX-based system by clustering sessions into groups bearing similar characteristics according to expertise and type of work. Our semi-supervised method based on Self-Organizing Map (SOM) accomplishes classification of four types of users: computer scientists, experience programmers, non-programmers, and novice programmers. Our evaluation on a range of biometrics shows that using working directories yields better accuracy (>98.5%) than using most popular parameters like command use or keystroke patterns. © 2009 IEEE. Fil:Segura, E.C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_97807695_v1_n_p48_Estrada
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Computer scientists
Computer sessions
Keystroke patterns
Novice programmer
Semi-supervised method
Artificial intelligence
Biometrics
Conformal mapping
Self organizing maps
spellingShingle Computer scientists
Computer sessions
Keystroke patterns
Novice programmer
Semi-supervised method
Artificial intelligence
Biometrics
Conformal mapping
Self organizing maps
Estrada, V.C.
Nakao, A.
Segura, E.C.
Classifying computer session data using self-organizing maps
topic_facet Computer scientists
Computer sessions
Keystroke patterns
Novice programmer
Semi-supervised method
Artificial intelligence
Biometrics
Conformal mapping
Self organizing maps
description We propose an advanced solution to track persistent computer intruders inside a UNIX-based system by clustering sessions into groups bearing similar characteristics according to expertise and type of work. Our semi-supervised method based on Self-Organizing Map (SOM) accomplishes classification of four types of users: computer scientists, experience programmers, non-programmers, and novice programmers. Our evaluation on a range of biometrics shows that using working directories yields better accuracy (>98.5%) than using most popular parameters like command use or keystroke patterns. © 2009 IEEE.
format CONF
author Estrada, V.C.
Nakao, A.
Segura, E.C.
author_facet Estrada, V.C.
Nakao, A.
Segura, E.C.
author_sort Estrada, V.C.
title Classifying computer session data using self-organizing maps
title_short Classifying computer session data using self-organizing maps
title_full Classifying computer session data using self-organizing maps
title_fullStr Classifying computer session data using self-organizing maps
title_full_unstemmed Classifying computer session data using self-organizing maps
title_sort classifying computer session data using self-organizing maps
url http://hdl.handle.net/20.500.12110/paper_97807695_v1_n_p48_Estrada
work_keys_str_mv AT estradavc classifyingcomputersessiondatausingselforganizingmaps
AT nakaoa classifyingcomputersessiondatausingselforganizingmaps
AT seguraec classifyingcomputersessiondatausingselforganizingmaps
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