ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)
http://airccse.org/journal/ijnsa.html
FORTIFICATION OF HYBRID INTRUSION DETECTION SYSTEM USING VARIANTS OF NEURAL NETWORKS AND SUPPORT VECTOR MACHINES
A. M. Chandrashekhar1 & K. Raghuveer2
1Department of Computer Science, Sri Jayachamarajendra College of Engineering (SJCE), Mysore, Karnataka, 570006, India
2Department of Information Science, National Institute of Engineering (NIE), Mysore, Karnataka 57008, India
ABSTRACT
Intrusion Detection Systems (IDS) form a key part of system defence, where it identifies abnormal activities happening in a computer system. In recent years different soft computing based techniques have been proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfect technology. This has provided an opportunity for data mining to make quite a lot of important contributions in the field of intrusion detection. In this paper we have proposed a new hybrid technique by utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Neurofuzzy and radial basis function(RBF) SVM for fortification of the intrusion detection system. The proposed technique has five major steps in which, first step is to perform the relevance analysis, and then input data is clustered using Fuzzy C-means clustering. After that, neuro-fuzzy is trained, such that each of the data point is trained with the corresponding neuro-fuzzy classifier associated with the cluster. Subsequently, a vector for SVM classification is formed and in the last step, classification using RBFSVM is performed to detect intrusion has happened or not. Data set used is the KDD cup 1999 dataset and we have used precision, recall, F-measure and accuracy as the evaluation metrics parameters. Our technique could achieve better accuracy for all types of intrusions. The results of proposed technique are compared with the other existing techniques. These comparisons proved the effectiveness of our technique.
Original Source URL: http://airccse.org/journal/nsa/0113nsa06.pdf
Volume Link: http://airccse.org/journal/jnsa13_current.html
http://airccse.org/journal/ijnsa.html
FORTIFICATION OF HYBRID INTRUSION DETECTION SYSTEM USING VARIANTS OF NEURAL NETWORKS AND SUPPORT VECTOR MACHINES
A. M. Chandrashekhar1 & K. Raghuveer2
1Department of Computer Science, Sri Jayachamarajendra College of Engineering (SJCE), Mysore, Karnataka, 570006, India
2Department of Information Science, National Institute of Engineering (NIE), Mysore, Karnataka 57008, India
ABSTRACT
Intrusion Detection Systems (IDS) form a key part of system defence, where it identifies abnormal activities happening in a computer system. In recent years different soft computing based techniques have been proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfect technology. This has provided an opportunity for data mining to make quite a lot of important contributions in the field of intrusion detection. In this paper we have proposed a new hybrid technique by utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Neurofuzzy and radial basis function(RBF) SVM for fortification of the intrusion detection system. The proposed technique has five major steps in which, first step is to perform the relevance analysis, and then input data is clustered using Fuzzy C-means clustering. After that, neuro-fuzzy is trained, such that each of the data point is trained with the corresponding neuro-fuzzy classifier associated with the cluster. Subsequently, a vector for SVM classification is formed and in the last step, classification using RBFSVM is performed to detect intrusion has happened or not. Data set used is the KDD cup 1999 dataset and we have used precision, recall, F-measure and accuracy as the evaluation metrics parameters. Our technique could achieve better accuracy for all types of intrusions. The results of proposed technique are compared with the other existing techniques. These comparisons proved the effectiveness of our technique.
Original Source URL: http://airccse.org/journal/nsa/0113nsa06.pdf
Volume Link: http://airccse.org/journal/jnsa13_current.html
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