Fuzzy rough sets: application to feature selection
Fuzzy Sets and Systems
Induction of fuzzy decision trees
Fuzzy Sets and Systems
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
A Unified Approach to Detecting Spatial Outliers
Geoinformatica
Algorithms for Spatial Outlier Detection
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Using Outlier Detection to Reduce False Positives in Intrusion Detection
NPC '08 Proceedings of the 2008 IFIP International Conference on Network and Parallel Computing
Algorithm for Fast Spatial Outlier Detection
ICYCS '08 Proceedings of the 2008 The 9th International Conference for Young Computer Scientists
A triangle area based nearest neighbors approach to intrusion detection
Pattern Recognition
A detailed analysis of the KDD CUP 99 data set
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
An Outlier Detection Method Based on Clustering
EAIT '11 Proceedings of the 2011 Second International Conference on Emerging Applications of Information Technology
Constructing a fuzzy decision tree by integrating fuzzy sets and entropy
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
Information Sciences: an International Journal
Attributes Reduction Using Fuzzy Rough Sets
IEEE Transactions on Fuzzy Systems
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In recent years, as the usage of internet increases, new type of attacks on network information is also increasing continuously. Intrusion Detection System (IDS) is an important component that provides security to the network information by identifying various kinds of attacks occurring in the networks. Currently, there are many researches who are working in this area and they focus on developing effective IDS using machine learning techniques. However, there is a need for better systems with improved detection accuracy and reduced false alarm rate. In this paper, we propose an Intelligent IDS using fuzzy rough set based C4.5 classification algorithm to improve the detection accuracy. This system has been compared with Support Vector Machines for illustrating the improvement with respect to the detection accuracy. The inputs to these classifiers were preprocessed using a fuzzy rough set based outlier detection algorithm. In this work, we used the KDD'99 Cup dataset for carrying out the simulation of the experiments. The experimental results obtained in this work show that the proposed model reduces the false alarm rate and improves overall detection accuracy.