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

机译:基于角色的分析使用模糊自适应共振理论来保护数据库系统

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摘要

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.
机译:研究界对数据库安全的研究界投入了很大的时间和精力,以实现安全和隐私的高度保证。安全数据库系统的一个重要组成部分是入侵检测系统,能够成功检测由应用和用户引起的异常行为。然而,在庞大的组织中建模大量用户的正常行为是非常不可行的并且效率低下。因此,本研究调查的主要目的是通过应用自适应谐振理论神经网络来模拟角色而不是用户的行为。观察到的行为,偏离任何既定的角色配置文件被视为恶意。该拟议的模型具有识别内幕威胁的优势,适用于大型组织,因为它基于角色分析而不是用户分析。所提出的系统能够以高精度检测入侵,以及最小化的误报。

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