Layered Based Classification Framework For Network Fault Management using Machine Learning
Mohammed Kamel Madi1, Khuzairi Mohd Zaini2, Amran Ahmad3, Suzi Iryanti4

1Mohammed Kamel Madi, Hassan Kalyoncu University, Computer Engineering Department, Gazi Antep, Turkey.

2Khuzairi Mohd Zaini, University Utara Malaysia, School of Computing, Kedah, Malaysia.

3Amran Ahmad, University Utara Malaysia, School of Computing, Kedah, Malaysia.

4Suzi Iryanti, University Sains Malaysia, School of computer science, Penang, Malaysia

Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript Published on 19 June 2019 | PP: 431-438 | Volume-8 Issue-8S June 2019 | Retrieval Number: H10750688S19/19©BEIESP

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Abstract: The ever-increasing amount of networking data as well as the complexity of telecommunication networks is also increasing, consequently the task of network management and troubleshooting is getting more complicated and difficult. 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. Detecting network problems could be more efficient if the detected problems have been classified in terms of network layers. In this paper, we focus on the usage of classification technique in the field of network management, more specifically in fault management. This paper proposes a layered based classification framework 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.

Keywords: About Four Key Words or Phrases in Alphabetical Order, Separated by Commas.
Scope of the Article: Machine Learning