Fortification of Hybrid Intrusion Detection System Using Variants of Neural Networks and Support Vector Machines
نویسندگان
چکیده
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.
منابع مشابه
Anomaly Detection Using SVM as Classifier and Decision Tree for Optimizing Feature Vectors
Abstract- With the advancement and development of computer network technologies, the way for intruders has become smoother; therefore, to detect threats and attacks, the importance of intrusion detection systems (IDS) as one of the key elements of security is increasing. One of the challenges of intrusion detection systems is managing of the large amount of network traffic features. Removing un...
متن کاملAn Efficient Hybrid Intrusion Detection System based on C5.0 and SVM
Nowadays, much attention has been paid to intrusion detection system (IDS) which is closely linked to the safe use of network services. Several machine-learning paradigms including neural networks, linear genetic programming (LGP), support vector machines (SVM), Bayesian networks, multivariate adaptive regression splines (MARS) fuzzy inference systems (FISs), etc. have been investigated for the...
متن کاملIntrusion Detection: Support Vector Machines and Neural Networks
This paper concerns intrusion detection and audit trail reduction. We describe approaches to intrusion detection and audit data reduction using support vector machines and neural networks. Using a set of benchmark data from the KDD (Knowledge Discovery and Data Mining) competition designed by DARPA, we demonstrate that efficient and highly accurate classifiers can be built using either support ...
متن کاملراهکار ترکیبی نوین جهت تشخیص نفوذ در شبکههای کامپیوتری با استفاده از الگوریتم-های هوش محاسباتی
In this paper, a novel hybrid method is proposed for intrusion detection in computer networks using combination of misuse-based and anomaly-based detection models with the aim of performance improvement. In the proposed hybrid approach, a set of algorithms and models is employed. The selection of input features is performed using shuffled frog-leaping (SFL) algorithm. The misuse detection modul...
متن کامل