Anomaly detection system using beta mixture models and outlier detection
Date
2018
Authors
Moustafa, N.
Creech, G.
Slay, J.
Editors
Pattnaik, P.K.
Rautaray, S.S.
Das, H.
Nayak, J.
Rautaray, S.S.
Das, H.
Nayak, J.
Advisors
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Type:
Conference paper
Citation
Advances in Intelligent Systems and Computing, 2018 / Pattnaik, P.K., Rautaray, S.S., Das, H., Nayak, J. (ed./s), vol.710, pp.125-135
Statement of Responsibility
Conference Name
International Conference on Computing, Analytics and Networking, ICCAN 2017 (15 Dec 2017 - 16 Dec 2017 : Bhubaneswar, India)
Abstract
An intrusion detection system (IDS) plays a significant role in recognising suspicious activities in hosts or networks, even though this system still has the challenge of producing high false positive rates with the degradation of its performance. This paper suggests a new beta mixture technique (BMM-ADS) using the principle of anomaly detection. This establishes a profile from the normal data and considers any deviation from this profile as an anomaly. The experimental outcomes show that the BMM-ADS technique provides a higher detection rate and lower false rate than three recent techniques on the UNSW-NB15 data set.
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Copyright 2018 Springer Nature