UUM Electronic Theses and Dissertation
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Network problems detection and classification by analyzing syslog data

Jarghon, Fidaa A. M. (2016) Network problems detection and classification by analyzing syslog data. Masters thesis, Universiti Utara Malaysia.

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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.

Item Type: Thesis (Masters)
Supervisor : Sainin, Mohammad Shamrie and Mohamad Tahir, Hatim
Item ID: 6541
Uncontrolled Keywords: Classification, SVM, Fault Detection
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7885-7895 Computer engineering. Computer hardware
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Awang Had Salleh Graduate School of Arts & Sciences
Date Deposited: 21 Nov 2017 01:36
Last Modified: 05 Apr 2021 02:43
Department: Awang Had Salleh Graduate School of Arts and Sciences
Name: Sainin, Mohammad Shamrie and Mohamad Tahir, Hatim
URI: https://etd.uum.edu.my/id/eprint/6541

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