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

Bayu Adhi Tama and Kyung-Hyune Rhee
Volume: 13, No: 5, Page: 1203 ~ 1212, Year: 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
IEEE Style
Bayu Adhi Tama and Kyung-Hyune 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, "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.