JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
A Detailed Analysis of Classifier Ensembles for Intrusion Detection in Wireless Network |
| 영문제목(English Title) |
A Detailed Analysis of Classifier Ensembles for Intrusion Detection in Wireless Network |
| 저자(Author) |
Bayu Adhi Tama
Kyung-Hyune Rhee
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| 원문수록처(Citation) |
VOL 13 NO. 05 PP. 1203 ~ 1212 (2017. 10) |
한글내용
(Korean Abstract) |
|
영문내용
(English 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. |
| 키워드(Keyword) |
Classifier Ensembles
Classifier’s Significance
Intrusion Detection Systems
IDSs
Wireless Network
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| 파일첨부 |
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