Anomaly detection using one-class SVM for logs of juniper router devices

Tat Bao Thien Nguyen, Teh Lu Liao, Tuan Anh Vu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The article deals with anomaly detection of Juniper router logs. Abnormal Juniper router logs include logs that are usually different from the normal operation, and they often reflect the abnormal operation of router devices. To prevent router devices from being damaged and help administrator to grasp the situation of error quickly, detecting abnormal operation soon is very important. In this work, we present a new way to get important features from log data of Juniper router devices and use machine learning method (basing on One-Class SVM model) for anomaly detection. One-Class SVM model requires some knowledge and comprehension about logs of Juniper router devices so that it can analyze, interpret, and test the knowledge acquired. We collect log data from a lot of real Juniper router devices and classify them based on our knowledge. Before these logs are used for training and testing the One-Class SVM model, the feature extraction phase for these data was carried out. Finally, with the proposed method, the system errors of the routers were detected quickly and accurately. This may help our company to reduce the operation cost for the router systems.

Original languageEnglish
Title of host publicationIndustrial Networks and Intelligent Systems - 5th EAI International Conference, INISCOM 2019, Proceedings
EditorsTrung Quang Duong, Nguyen-Son Vo, Loi K. Nguyen, Quoc-Tuan Vien, Van-Dinh Nguyen
PublisherSpringer Verlag
Pages302-312
Number of pages11
ISBN (Print)9783030301484
DOIs
Publication statusPublished - 2019 Jan 1
Event5th EAI International Conference on Industrial Networks and Intelligent Systems, INISCOM 2019 - Ho Chi Minh City, Viet Nam
Duration: 2019 Aug 192019 Aug 19

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume293
ISSN (Print)1867-8211

Conference

Conference5th EAI International Conference on Industrial Networks and Intelligent Systems, INISCOM 2019
CountryViet Nam
CityHo Chi Minh City
Period19-08-1919-08-19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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