A Detailed Analysis of Classifier Ensembles for Intrusion Detection in Wireless Network


Bayu Adhi Tama, Kyung-Hyune Rhee, Journal of Information Processing Systems Vol. 13, No. 5, pp. 1203-1212, Oct. 2017  

10.3745/JIPS.03.0080
Keywords: Classifier Ensembles, Classifier’s Significance, Intrusion Detection Systems (IDSs), Wireless Network
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Abstract

Intrusion detection systems (IDSs) are crucial in this overwhelming increase of attacks on the computing infrastructure. It intelligently detects malicious and predicts future attack patterns based on the classification analysis using machine learning and data mining techniques. This paper is devoted to thoroughly evaluate classifier ensembles for IDSs in IEEE 802.11 wireless network. Two ensemble techniques, i.e. voting and stacking are employed to combine the three base classifiers, i.e. decision tree (DT), random forest (RF), and support vector machine (SVM). We use area under ROC curve (AUC) value as a performance metric. Finally, we conduct two statistical significance tests to evaluate the performance differences among classifiers.


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Cite this article
[APA Style]
Bayu Adhi Tama and Kyung-Hyune Rhee (2017). A Detailed Analysis of Classifier Ensembles for Intrusion Detection in Wireless Network. Journal of Information Processing Systems, 13(5), 1203-1212. DOI: 10.3745/JIPS.03.0080.

[IEEE Style]
B. A. Tama and K. Rhee, "A Detailed Analysis of Classifier Ensembles for Intrusion Detection in Wireless Network," Journal of Information Processing Systems, vol. 13, no. 5, pp. 1203-1212, 2017. DOI: 10.3745/JIPS.03.0080.

[ACM Style]
Bayu Adhi Tama and Kyung-Hyune Rhee. 2017. A Detailed Analysis of Classifier Ensembles for Intrusion Detection in Wireless Network. Journal of Information Processing Systems, 13, 5, (2017), 1203-1212. DOI: 10.3745/JIPS.03.0080.