Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System
Küçük Resim Yok
Tarih
2016
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieice-Inst Electronics Information Communications Eng
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
With the increase of network components connected to the Internet, the need to ensure secure connectivity is becoming increasingly vital. Intrusion Detection Systems (IDSs) are one of the common security components that identify security violations. This paper proposes a novel multilevel hybrid classifier that uses different feature sets on each classifier. It presents the Discernibility Function based Feature Selection method and two classifiers involving multilayer perceptron (MLP) and decision tree (C4.5). Experiments are conducted on the KDD'99 Cup and ISCX datasets, and the proposal demonstrates better performance than individual classifiers and other proposed hybrid classifiers. The proposed method provides significant improvement in the detection rates of attack classes and Cost Per Example (CPE) which was the primary evaluation method in the KDD'99 Cup competition.
Açıklama
Anahtar Kelimeler
Intrusion Detection, Discernibility Function, Feature Selection, Neural Networks
Kaynak
Ieice Transactions On Information And Systems
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
E99D
Sayı
7
