A Novel Classification Method Using Hybridization of Fuzzy Clustering and Neural Networks for Intrusion Detection

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Saeed Khazaee 1,* Karim Faez 2

1. Engineering Department, Islamic Azad University, Chalous Branch, Iran

2. Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2014.11.02

Received: 12 Aug. 2014 / Revised: 11 Sep. 2014 / Accepted: 2 Oct. 2014 / Published: 8 Nov. 2014

Index Terms

Intrusion detection system, fuzzy clustering, neural network, classification, regression


In this paper, a hybrid classifier using fuzzy clustering and several neural networks has been proposed. With using the fuzzy C-means algorithm, training samples will be clustered and the inappropriate data will be detected and moved to another dataset (Removed-Dataset) and used differently in the classification phase. Also, in the proposed method using the membership degree of samples to the clusters, the class of samples will be changed to the fuzzy class. Thus, for example in KDD cup99 dataset, any sample will have 5 membership degrees to classes DoS, Probe, Normal, U2R, and R2L. Afterwards, the neural networks will be trained by new labels then using a combination of regression and classification methods, the hybrid classifier will be created. Also to classify the outlier data, a fuzzy ARTMAP neural network is employed which is a part of the hybrid classifier.
Evaluation of the proposed method is performed by KDDCup99 dataset for intrusion detection and Cambridge datasets for traffic classification problems. Our experimental results indicate that the proposed system has performed better than the previous works in the case of precision, recall and f-value also detection and false alarm rate. Also, ROC curve analysis shows that the proposed hybrid classifier has been better than the famous non-hybrid classifiers.

Cite This Paper

Saeed Khazaee, Karim Faez, "A Novel Classification Method Using Hybridization of Fuzzy Clustering and Neural Networks for Intrusion Detection", International Journal of Modern Education and Computer Science (IJMECS), vol.6, no.11, pp.11-24, 2014. DOI:10.5815/ijmecs.2014.11.02


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