Comparative analysis of classification techniques for network fault management

Mohammed MADI; Fıdaa JARGHON; Yousef FAZEA; Omar ALMOMANI; Adeeb SAAIDAH

doi:https://doi.org/10.3906/elk-1907-84

Makale detayları

Comparative analysis of classification techniques for network fault management

Yazarlar:

Mohammed MADI | Fıdaa JARGHON | Yousef FAZEA | Omar ALMOMANI | Adeeb SAAIDAH

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2020 Vol.28 Issue 3, pp.1442-1457

Belge Tipi:

Makale

Yayın Tarihi:

4/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000733116

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

DOI:

https://doi.org/10.3906/elk-1907-84

Özet:

 

Network troubleshooting is a significant process. Many studies were conducted about it. The first step in  the troubleshooting procedures is represented in collecting information. It's collected in order to identify the problems.  Syslog messages which are sent by almost all network devices include a massive amount of data that concern the network  problems. Based on several studies, it was found that analyzing syslog data  which  can be a guideline for network  problems and their causes. The detection of network problems can become more efficient if the detected problems have  been classified based on the network layers. Classifying syslog data requires identifying the syslog messages that describe  the network problems for each layer. It also requires taking into account the formats of syslog for vendors' devices. The  present study aimed to propose a method for classifying the syslog messages which identify the network problem.This  classification is conducted based on the network layers. This method uses data mining instrument to classify the syslog  messages. The description part of the syslog message was used for carrying out the classification process.The relevant  syslog messages were identified. The 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 was obtained from an  educational network device. This dataset was used for the prediction stage. It was found that that LibSVM outperforms  other classifiers in terms of the probability rate of the classified instances where it was in the range of 89.90%-32.80%.  Furthermore, the validation results indicate that the probability rate of the correctly classified instances is >70%.

Makale detayları

Comparative analysis of classification techniques for network fault management

Yazarlar: Mohammed MADI | Fıdaa JARGHON | Yousef FAZEA | Omar ALMOMANI | Adeeb SAAIDAH

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2020 Vol.28 Issue 3, pp.1442-1457

Belge Tipi: Makale

Yayın Tarihi: 4/2020

Belge Dili: İngilizce

Document ID (Org No.): 2000733116

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

DOI: https://doi.org/10.3906/elk-1907-84

Sayı

Yazarlar:

Mohammed MADI | Fıdaa JARGHON | Yousef FAZEA | Omar ALMOMANI | Adeeb SAAIDAH

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2020 Vol.28 Issue 3, pp.1442-1457

Belge Tipi:

Makale

Yayın Tarihi:

4/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000733116

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

DOI:

https://doi.org/10.3906/elk-1907-84