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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(2): 137-141 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.497

Evaluation of Machine Learning Method for Intrusion Detection System on Jubatus

Tadashi Ogino
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.

T. Ogino is with the Okinawa National College of Technology, Nago, Okinawa 905-2192, Japan (e-mail: ogino@okinawa-ct.ac.jp).

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Cite: Tadashi Ogino, "Evaluation of Machine Learning Method for Intrusion Detection System on Jubatus," International Journal of Machine Learning and Computing vol. 5, no. 2, pp. 137-141, 2015.

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