New Notions for Fuzzy Equivalence Using α-cut Relation

Kannan, S.; Mohapatra, Rajesh Kumar

doi:10.1088/1742-6596/1344/1/012040

Cited by 2 publications

(5 citation statements)

References 10 publications

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“…Zadeh et al [37] introduced the details of fuzzy similarity relations. In [38], the concepts of α-cut of a fuzzy relation and fuzzy equivalence relations were introduced in detail.…”

Section: Related Workmentioning

confidence: 99%

“…Proposition 1. [37,38] If a fuzzy relation R is an equivalence relation in max-min sense, then for α ∈ (0, 1), R α possesses an equivalence relation. Therefore, any α-relation represented by an α-cut R α will have an equivalence relation.…”

Section: Definitionmentioning

confidence: 99%

“…In [36], the authors proposed the formula for correlation coefficient of intuitionistic fuzzy sets whose value lies in the interval [0, 1]. Fuzzy relation, α-cut of a fuzzy relation, and fuzzy equivalence relations were introduced in [37,38].…”

Section: Introductionmentioning

confidence: 99%

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An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection

Mazarbhuiya

Sahmoudi

2023

Applied Sciences

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The challenging issues of computer networks and databases are not only the intrusion detection but also the reduction of false positives 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 threats. Several techniques have already been successfully tried for this purpose. However, the normal and suspicious behaviors are hard to predict as there is no precise boundary differentiating one from another. Here, rough set theory and fuzzy set theory come into the picture. In this article, a hybrid approach consisting of rough set theory and intuitionistic fuzzy set theory is proposed for the detection of anomaly. The proposed approach is a classification approach which takes the advantages of both rough set and intuitionistic fuzzy set to deal with inherent uncertainty, vagueness, and indiscernibility in the dataset. The algorithm classifies the data instances in such a way that they can be expressed using natural language. A data instance can possibly or certainly belong to a class with degrees of membership and non-membership. The empirical study with a real-world and a synthetic dataset demonstrates 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.

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“…Zadeh et al [37] introduced the details of fuzzy similarity relations. In [38], the concepts of α-cut of a fuzzy relation and fuzzy equivalence relations were introduced in detail.…”

Section: Related Workmentioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

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An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection

Mazarbhuiya

Sahmoudi

2023

Applied Sciences

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“…In [31], the authors proposed the formula for correlation coefficient of intuitionistic fuzzy sets whose value lies between 0 and 1. Fuzzy relation, -cut of a fuzzy relation and fuzzy equivalence relations were introduced in [32,33].…”

Section: Of 20mentioning

confidence: 99%

“…In [32], the authors introduced the details of fuzzy similarity relations. In [33], the concepts of -cut of a fuzzy relation and fuzzy equivalence relations were introduced in detail.…”

Section: Related Workmentioning

confidence: 99%

An Intuitionistic Fuzzy Rough Set Based Classification for Anomaly Detection

Mazarbhuiya¹,

Sahmoudi²

2023

Preprint

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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

show abstract

New Notions for Fuzzy Equivalence Using α-cut Relation

Cited by 2 publications

References 10 publications

An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection

An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection

An Intuitionistic Fuzzy Rough Set Based Classification for Anomaly Detection

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