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Network problems detection and classification by analyzing syslog data

Network troubleshooting is an important process which has a wide research field. The first step in troubleshooting procedures is to collect information in order to diagnose the problems. Syslog messages which are sent by almost all network devices contain a massive amount of data related to the netw...

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Main Author:	Jarghon, Fidaa A. M.
Format:	Thesis
Language:	English English
Published:	2016
Subjects:	TK7885-7895 Computer engineering. Computer hardware QA75 Electronic computers. Computer science
Online Access:	http://etd.uum.edu.my/6541/
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id	my.uum.etd.6541
record_format	eprints
spelling	my.uum.etd.65412021-04-05T02:43:25Z http://etd.uum.edu.my/6541/ Network problems detection and classification by analyzing syslog data Jarghon, Fidaa A. M. TK7885-7895 Computer engineering. Computer hardware QA75 Electronic computers. Computer science Network troubleshooting is an important process which has a wide research field. The first step in troubleshooting procedures is to collect information in order to diagnose the problems. Syslog messages which are sent by almost all network devices contain a massive amount of data related to the network problems. It is found that in many studies conducted previously, analyzing syslog data which can be a guideline for network problems and their causes was used. Detecting network problems could be more efficient if the detected problems have been classified in terms of network layers. Classifying syslog data needs to identify the syslog messages that describe the network problems for each layer, taking into account the different formats of various syslog for vendors’ devices. This study provides a method to classify syslog messages that indicates the network problem in terms of network layers. The method used data mining tool to classify the syslog messages while the description part of the syslog message was used for classification process. Related syslog messages were identified; features were then selected to train the classifiers. Six classification algorithms were learned; LibSVM, SMO, KNN, Naïve Bayes, J48, and Random Forest. A real data set which was obtained from the Universiti Utara Malaysia’s (UUM) network devices is used for the prediction stage. Results indicate that SVM shows the best performance during the training and prediction stages. This study contributes to the field of network troubleshooting, and the field of text data classification. 2016 Thesis NonPeerReviewed text en /6541/1/s815675_01.pdf text en /6541/2/s815675_02.pdf Jarghon, Fidaa A. M. (2016) Network problems detection and classification by analyzing syslog data. Masters thesis, Universiti Utara Malaysia.
institution	Universiti Utara Malaysia
building	UUM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Utara Malaysia
content_source	UUM Electronic Theses
url_provider	http://etd.uum.edu.my/
language	English English
topic	TK7885-7895 Computer engineering. Computer hardware QA75 Electronic computers. Computer science
spellingShingle	TK7885-7895 Computer engineering. Computer hardware QA75 Electronic computers. Computer science Jarghon, Fidaa A. M. Network problems detection and classification by analyzing syslog data
description	Network troubleshooting is an important process which has a wide research field. The first step in troubleshooting procedures is to collect information in order to diagnose the problems. Syslog messages which are sent by almost all network devices contain a massive amount of data related to the network problems. It is found that in many studies conducted previously, analyzing syslog data which can be a guideline for network problems and their causes was used. Detecting network problems could be more efficient if the detected problems have been classified in terms of network layers. Classifying syslog data needs to identify the syslog messages that describe the network problems for each layer, taking into account the different formats of various syslog for vendors’ devices. This study provides a method to classify syslog messages that indicates the network problem in terms of network layers. The method used data mining tool to classify the syslog messages while the description part of the syslog message was used for classification process. Related syslog messages were identified; features were then selected to train the classifiers. Six classification algorithms were learned; LibSVM, SMO, KNN, Naïve Bayes, J48, and Random Forest. A real data set which was obtained from the Universiti Utara Malaysia’s (UUM) network devices is used for the prediction stage. Results indicate that SVM shows the best performance during the training and prediction stages. This study contributes to the field of network troubleshooting, and the field of text data classification.
format	Thesis
author	Jarghon, Fidaa A. M.
author_facet	Jarghon, Fidaa A. M.
author_sort	Jarghon, Fidaa A. M.
title	Network problems detection and classification by analyzing syslog data
title_short	Network problems detection and classification by analyzing syslog data
title_full	Network problems detection and classification by analyzing syslog data
title_fullStr	Network problems detection and classification by analyzing syslog data
title_full_unstemmed	Network problems detection and classification by analyzing syslog data
title_sort	network problems detection and classification by analyzing syslog data
publishDate	2016
url	http://etd.uum.edu.my/6541/
_version_	1696978323496763392
score	13.211869

Network problems detection and classification by analyzing syslog data

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