Insider threat prevention in distributed database as a service cloud environment. (July 2022)

• Record Type: Journal Article
• Authors: Deep, Gaurav; Sidhu, Jagpreet; Mohana, Rajni
• Is Part Of: Computers & industrial engineering. Volume 169(2022)
• Publication Date: 2022-07
• Subjects: Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108278
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Proposed and validated a technique for authentication of user in cloud environment. Proposed a blockchain-based framework for authenticating and achieve traceability. Developed and deployed a distributed PEP-PDP model for access control for DaaS. Solve scalability and ensuring transparency in contrast to traditional method. Designed and developed a tracking system for detecting insider attacks for DaaS. Abstract: This paper addresses the problem of increasing chances of insider threats in cloud computing. In Database as a Service (DBaaS), where multiple dependencies exist among data. Insiders tries to infer knowledge on the bases of dependencies. There is a requirement of Insider threat prevention mechanism for DBaaS due to its dynamic nature. Various researchers have proposed a Single PDP and Multiple PEPs to solve this problem with the limitations on authentication, scalability, tracking of insiders. This research aims to fill the gap by providing an effective framework on access control mechanism designed to prevent insider threats using a Blockchain-based authentication mechanism, a distributed architecture-based, and insider activity tracking mechanism. The framework enables us to handle authentication mechanism, distributed DBaaS insider activity tracking. Experimental results show that this framework is effective, dynamic and scalable. The validation of this framework has been done with the help of synthetic data due to lack of standardized dataset and its applicability has been demonstrated with the help of a case study involving the use of real cloud data.