Intrusion Detection and Risk Evaluation in Online Transactions Using Partitioning Methods

Yazdani, Hossein; Choroś, Kazimierz

doi:10.1007/978-3-319-98678-4_21

Cited by 2 publications

(5 citation statements)

References 16 publications

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“…The origin can participate in all clusters (lines) as a full member. These objects such as the origin and alike are called critical objects [32]. Addressing the critical objects is very important, as we need to encourage or prevent objects to/from participating in other clusters.…”

Section: New Learning Methodology (Bfpm)mentioning

confidence: 99%

“…The proposed method has been applied on real applications and problems from different domains such as medicine (lung cancer diagnosis) [30], intrusion detection systems [32], and banking (financial) systems and risk managements (decision making systems) [35]. The proposed methodology has been utilized in both supervised and unsupervised learning strategies, but in this paper clustering problems have been selected for experimental verifications.…”

Section: Experimental Verificationmentioning

confidence: 99%

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Bounded Fuzzy Possibilistic Method

Yazdani

2020

Fuzzy Sets and Systems

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This paper introduces Bounded Fuzzy Possibilistic Method (BFPM) by addressing several issues that previous clustering/classification methods have not considered. In fuzzy clustering, object's membership values should sum to 1. Hence, any object may obtain full membership in at most one cluster. Possibilistic clustering methods remove this restriction. However, BFPM differs from previous fuzzy and possibilistic clustering approaches by allowing the membership function to take larger values with respect to all clusters. Furthermore, in BFPM, a data object can have full membership in multiple clusters or even in all clusters. BFPM relaxes the boundary conditions (restrictions) in membership assignment. The proposed methodology satisfies the necessity of obtaining full memberships and overcomes the issues with conventional methods on dealing with overlapping. Analysing the objects' movements from their own cluster to another (mutation) is also proposed in this paper. BFPM has been applied in different domains in geometry, set theory, anomaly detection, risk management, diagnosis diseases, and other disciplines. Validity and comparison indexes have been also used to evaluate the accuracy of BFPM. BFPM has been evaluated in terms of accuracy, fuzzification constant (different norms), object's movement analysis, and covering diversity. The promising results prove the importance of considering the proposed methodology in learning methods to track the behaviour of data objects, in addition to obtain accurate results.

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Section: New Learning Methodology (Bfpm)mentioning

confidence: 99%

Section: Experimental Verificationmentioning

confidence: 99%

Bounded Fuzzy Possibilistic Method

Yazdani

2020

Fuzzy Sets and Systems

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“…The chapter demonstrates the achievements of the proposed method and the introduced similarity and membership functions in comparison with other methods. The proposed method has been already published in the Fuzzy Sets and Systems Journal [11] as well as other ideas in different papers cited at the end of the Thesis [12], [13][14][15][16][17][18][19][20], [21].…”

Section: Thesis Organizationmentioning

confidence: 99%

“…Regarding the cyber threat taxonomy [12], different types of threat can be detected and prevented using machine learning approaches. Further more, the Thesis considers both classification and clustering approaches in its experimental verifications to evaluate and illustrate the ability of the proposed methods in intrusion detection and prevention systems.…”

Section: Example In Security and Risk Managementmentioning

confidence: 99%

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Applying bounded fuzzy possibilistic method on critical objects

Yazdani

Ortiz-Arroyo

Choroś

et al. 2016

2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI)

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Providing a flexible environment to process data objects is a desirable goal of machine learning algorithms. In fuzzy and possibilistic methods, the relevance of data objects is evaluated and a membership degree is assigned. However, some critical objects objects have the potential ability to affect the performance of the clustering algorithms if they remain in a specific cluster or they are moved into another. In this paper we analyze and compare how critical objects affect the behaviour of fuzzy possibilistic methods in several data sets. The comparison is based on the accuracy and ability of learning methods to provide a proper searching space for data objects. The membership functions used by each method when dealing with critical objects is also evaluated. Our results show that relaxing the conditions of participation for data objects in as many partitions as they can, is beneficial.

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Intrusion Detection and Risk Evaluation in Online Transactions Using Partitioning Methods

Cited by 2 publications

References 16 publications

Bounded Fuzzy Possibilistic Method

Bounded Fuzzy Possibilistic Method

Applying bounded fuzzy possibilistic method on critical objects

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