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Role-Based Profiling Using Fuzzy Adaptive Resonance Theory for Securing Database Systems

Role-Based Profiling Using Fuzzy Adaptive Resonance Theory for Securing Database Systems

Anitarani Brahma, Suvasini Panigrahi
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 13
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799861133|DOI: 10.4018/IJAMC.2021040103
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MLA

Brahma, Anitarani, and Suvasini Panigrahi. "Role-Based Profiling Using Fuzzy Adaptive Resonance Theory for Securing Database Systems." IJAMC vol.12, no.2 2021: pp.36-48. http://doi.org/10.4018/IJAMC.2021040103

APA

Brahma, A. & Panigrahi, S. (2021). Role-Based Profiling Using Fuzzy Adaptive Resonance Theory for Securing Database Systems. International Journal of Applied Metaheuristic Computing (IJAMC), 12(2), 36-48. http://doi.org/10.4018/IJAMC.2021040103

Chicago

Brahma, Anitarani, and Suvasini Panigrahi. "Role-Based Profiling Using Fuzzy Adaptive Resonance Theory for Securing Database Systems," International Journal of Applied Metaheuristic Computing (IJAMC) 12, no.2: 36-48. http://doi.org/10.4018/IJAMC.2021040103

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Abstract

Very large amounts of time and effort have been invested by the research community working on database security to achieve high assurance of security and privacy. An important component of a secure database system is intrusion detection system which has the ability to successfully detect anomalous behavior caused by applications and users. However, modeling the normal behavior of a large number of users in a huge organization is quite infeasible and inefficient. The main purpose of this research investigation is thus to model the behavior of roles instead of users by applying adaptive resonance theory neural network. The observed behavior which deviates from any of the established role profiles is treated as malicious. The proposed model has the advantage of identifying insider threat and is applicable for large organizations as it is based on role profiling instead of user profiling. The proposed system is capable of detecting intrusion with high accuracy along with minimized false alarms.

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