Article: An optimal insider threat detection model based on improved deep belief network with feature reduction scheme for e-healthcare system Journal: International Journal of System of Systems Engineering (IJSSE) 2025 Vol.15 No.3 pp.246 - 268 Abstract: An electronic health records (EHR) dataset contains routine actions performed when accessing a patient's record, Patient user information corresponds to the record's identifier, whereas device data describes how the record was accessed. Leakage of valuable information is a critical challenge. As the Internet-of-Things (IoT) evolves, new security challenges arise in existing security architectures. An organisation's insider threat management is at risk because attack surfaces have expanded dramatically. To solve the above challenges, an optimised deep belief network (DBN) is proposed to detect insider threats in EHR. Significant features are generated using correlation coefficients, random forest mean reduced accuracy, and gain ratio to improve the performance of the internal threat detection model. An appropriate mechanism (and function) is then used to combine the features to obtain an optimal set of features. Adaptive rat optimisation algorithm (AROA) optimises DBN weight parameters to enhance performance. F-measure, accuracy, and G-mean are calculated to measure Performance. Inderscience Publishers - linking academia, business and industry through research

Title: An optimal insider threat detection model based on improved deep belief network with feature reduction scheme for e-healthcare system

Authors: M. Madhavi; T. SasiRooba; G. Kranthi Kumar

Addresses: Department of Computer Science and Engineering, Annamalai University, Chidambaram, 608002, Tamil Nadu, India ' Department of Computer Science and Engineering, Annamalai University, Chidambaram, 608002, Tamil Nadu, India ' Department of Computer Science and Engineering, V.R. Siddhartha Engineering College, Vijayawada, 520007, AP, India

Abstract: An electronic health records (EHR) dataset contains routine actions performed when accessing a patient's record, Patient user information corresponds to the record's identifier, whereas device data describes how the record was accessed. Leakage of valuable information is a critical challenge. As the Internet-of-Things (IoT) evolves, new security challenges arise in existing security architectures. An organisation's insider threat management is at risk because attack surfaces have expanded dramatically. To solve the above challenges, an optimised deep belief network (DBN) is proposed to detect insider threats in EHR. Significant features are generated using correlation coefficients, random forest mean reduced accuracy, and gain ratio to improve the performance of the internal threat detection model. An appropriate mechanism (and function) is then used to combine the features to obtain an optimal set of features. Adaptive rat optimisation algorithm (AROA) optimises DBN weight parameters to enhance performance. F-measure, accuracy, and G-mean are calculated to measure Performance.

Keywords: HER; electronic health records; insider threat detection; DBN; deep belief network; AROA; adaptive rat optimisation algorithm; single optimised feature sets.

DOI: 10.1504/IJSSE.2025.147018

International Journal of System of Systems Engineering, 2025 Vol.15 No.3, pp.246 - 268

Received: 29 May 2023
Accepted: 07 Jun 2023
Published online: 10 Jul 2025 *

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Title: An optimal insider threat detection model based on improved deep belief network with feature reduction scheme for e-healthcare system

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