Volume 10 Number 6 (Jun. 2015)
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JSW 2015 Vol.10(6): 748-756 ISSN: 1796-217X
doi: 10.17706/jsw.10.6.748-756

An Evaluation of Machine Learning Method for Intrusion Detection System Using LOF on Jubatus

Tadashi Ogino*
Okinawa National College of Technology, Okinawa, Japan.

Abstract—The network intrusion is becoming a big threat for a lot of companies, organization and so on. Recent intrusions are becoming more clever and difficult to detect. Many of today’s intrusion detection systems are based on signature-based. They have good performance for known attacks, but theoretically they are not able to detect unknown attacks. On the other hand, an anomaly detection system can detect unknown attacks and is getting focus recently. We study an anomaly detection system as one application area of machine learning technology. In this paper, we study the effectiveness and the performance experiments of one of the major anomaly detection scales, LOF, on distributed online machine learning framework, Jubatus. After basic experiment, we propose a new machine learning method and show our new method has a better performance than the original method.

Index Terms—Anomaly detection, machine learning, Jubatus, LOF.


Cite: Tadashi Ogino, "An Evaluation of Machine Learning Method for Intrusion Detection System Using LOF on Jubatus," Journal of Software vol. 10, no. 6, pp. 748-756, 2015.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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