Applications of Data Mining in Computer Security
Applications of Data Mining in Computer Security
Enhancing profiles for anomaly detection using time granularities
Journal of Computer Security
Proceedings of the first international conference on Neutrosophy, neutrosophic logic, neutrosophic set, neutrosophic probability and statistics
Introduction to neutrosophic logic
Introduction to neutrosophic logic
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
Distributed agents model for intrusion detection based on AIS
Knowledge-Based Systems
Fuzzy Sets and Systems
An Empirical Approach to Modeling Uncertainty in Intrusion Analysis
ACSAC '09 Proceedings of the 2009 Annual Computer Security Applications Conference
Finding key attribute subset in dataset for outlier detection
Knowledge-Based Systems
Evaluation of classification algorithms for intrusion detection in MANETs
Knowledge-Based Systems
A competitive ensemble pruning approach based on cross-validation technique
Knowledge-Based Systems
An effective ensemble pruning algorithm based on frequent patterns
Knowledge-Based Systems
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In the real world it is a routine that one must deal with uncertainty when security is concerned. Intrusion detection systems offer a new challenge in handling uncertainty due to imprecise knowledge in classifying the normal or abnormal behaviour patterns. In this paper we have introduced an emerging approach for intrusion detection system using Neutrosophic Logic Classifier which is an extension/combination of the fuzzy logic, intuitionistic logic, paraconsistent logic, and the three-valued logics that use an indeterminate value. It is capable of handling fuzzy, vague, incomplete and inconsistent information under one framework. Using this new approach there is an increase in detection rate and the significant decrease in false alarm rate. The proposed method tripartitions the dataset into normal, abnormal and indeterministic based on the degree of membership of truthness, degree of membership of indeterminacy and degree of membership of falsity. The proposed method was tested up on KDD Cup 99 dataset. The Neutrosophic Logic Classifier generates the Neutrosophic rules to determine the intrusion in progress. Improvised genetic algorithm is adopted in order to detect the potential rules for performing better classification. This paper exhibits the efficiency of handling uncertainty in Intrusion detection precisely using Neutrosophic Logic Classifier based Intrusion detection System.