A Detailed Analysis of Classifier Ensembles for Intrusion Detection in Wireless Network
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초록
Intrusion detection systems (IDSs) are crucial in this overwhelming increase of attacks on the computinginfrastructure. It intelligently detects malicious and predicts future attack patterns based on the classificationanalysis using machine learning and data mining techniques. This paper is devoted to thoroughly evaluateclassifier ensembles for IDSs in IEEE 802.11 wireless network. Two ensemble techniques, i.e. voting andstacking are employed to combine the three base classifiers, i.e. decision tree (DT), random forest (RF), andsupport 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.
Abstract
Intrusion detection systems (IDSs) are crucial in this overwhelming increase of attacks on the computinginfrastructure. It intelligently detects malicious and predicts future attack patterns based on the classificationanalysis using machine learning and data mining techniques. This paper is devoted to thoroughly evaluateclassifier ensembles for IDSs in IEEE 802.11 wireless network. Two ensemble techniques, i.e. voting andstacking are employed to combine the three base classifiers, i.e. decision tree (DT), random forest (RF), andsupport 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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