Article Version 1 Preserved in Portico This version is not peer-reviewed
An Intuitionistic Fuzzy Rough Set Based Classification for Anomaly Detection
Version 1 : Received: 27 March 2023 / Approved: 28 March 2023 / Online: 28 March 2023 (12:46:08 CEST)
A peer-reviewed article of this Preprint also exists.
Mazarbhuiya, F.A.; Shenify, M. An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection. Appl. Sci. 2023, 13, 5578. Mazarbhuiya, F.A.; Shenify, M. An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection. Appl. Sci. 2023, 13, 5578.
The challenging issues of Computer Network and Databases are not only the intrusion detection but also the reduction of false positive and increase of detection rate. In any intrusion detection system, anomaly detection mainly focuses on modeling the normal behavior of the users and detecting the deviations from normal behavior which are assumed to be potential intrusions or treat. Several techniques have already been successfully tried for this purpose. However, the normal and suspicious behavior are hard to predict as there is no precise boundary differentiat-ing one from another. Here rough set theory and fuzzy set theory come into the picture. In this article, a hybrid approach based on rough set theory and intuitionistic fuzzy set theory is pro-posed for the detection of anomaly. The proposed approach is a classification approach which takes the advantages of softness properties both rough and fuzzy set theory to deal with uncer-tainty in the dataset. The algorithm classifies the data instances in such a way that they can be expressed using natural language. The experimental results with a real world dataset and a syn-thetic dataset show that the proposed algorithm has normal true positive rates of 91.989% and 96.99% and attack true positive rates of 91.289% and 96.29% respectively
Intuitionistic fuzzy sets; Fuzzy correlation; Fuzzy relation; -cut of a fuzzy relation; Similarity relation; Fuzzy lower and upper Approximation of sets.
Computer Science and Mathematics, Software
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