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Volume 9
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Article

Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine

by
Ansam Khraisat
*,
Iqbal Gondal
,
Peter Vamplew
,
Joarder Kamruzzaman
and
Ammar Alazab
Internet Commerce Security Laboratory, Federation University Australia, Mount Helen 3350, Australia
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(1), 173; https://doi.org/10.3390/electronics9010173
Submission received: 10 January 2020 / Revised: 14 January 2020 / Accepted: 14 January 2020 / Published: 17 January 2020
(This article belongs to the Special Issue Machine Learning Techniques for Intelligent Intrusion Detection Systems)
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Abstract

Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates.
Keywords: anomaly detection; hybrid approach; C5.0 Decision tree; Cyber analytics; data mining; machine learning; Zero-day malware; Intrusion; Intrusion Detection System anomaly detection; hybrid approach; C5.0 Decision tree; Cyber analytics; data mining; machine learning; Zero-day malware; Intrusion; Intrusion Detection System

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MDPI and ACS Style

Khraisat, A.; Gondal, I.; Vamplew, P.; Kamruzzaman, J.; Alazab, A. Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine. Electronics 2020, 9, 173. https://doi.org/10.3390/electronics9010173

AMA Style

Khraisat A, Gondal I, Vamplew P, Kamruzzaman J, Alazab A. Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine. Electronics. 2020; 9(1):173. https://doi.org/10.3390/electronics9010173

Chicago/Turabian Style

Khraisat, Ansam, Iqbal Gondal, Peter Vamplew, Joarder Kamruzzaman, and Ammar Alazab. 2020. "Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine" Electronics 9, no. 1: 173. https://doi.org/10.3390/electronics9010173

APA Style

Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., & Alazab, A. (2020). Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine. Electronics, 9(1), 173. https://doi.org/10.3390/electronics9010173

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