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  • 标题:A Comparative Study of NOSQL System Vulnerabilities with Big Data
  • 本地全文:下载
  • 作者:Kiran Fahd ; Sitalakshmi Venkatraman ; Fahd Khan Hammeed
  • 期刊名称:International Journal of Managing Information Technology
  • 印刷版ISSN:0975-5926
  • 电子版ISSN:0975-5586
  • 出版年度:2019
  • 卷号:11
  • 期号:4
  • 页码:1-19
  • DOI:10.5121/ijmit.2019.11401
  • 出版社:Academy & Industry Research Collaboration Center (AIRCC)
  • 摘要:With the emerging third wave in the development of the Internet, the past year has witnessed huge data exposure resulting in cyber-attacks that have increased four times as that of the previous year’s record. In this digital era, businesses are making use of NoSQL technologies for managing such Big Data. However, the NoSQL database systems come with inherent security issues, which pose a major challenge to many organisations worldwide. There is a paucity of research studies for exposing the security threats and vulnerabilities of NoSQL technologies comprehensively. This paper presents an in-depth study of NoSQL security issues by performing a detailed comparative study of the security vulnerabilities identified in NoSQL database systems. A set of key security features offered by the four commonly used NoSQL database systems, namely Redis, Cassandra, MongoDB and Neo4j are analysed with an aim to identify their strengths and weaknesses. The vulnerabilities associated with built-in security, encryption, authentication/authorization and auditing that impact Big Data management are compared among these popular NoSQL database systems and the risk levels are identified. In addition, illustrations of possible injection attacks experimented with these NoSQL systems are provided. Finally, a high-level framework is proposed for NoSQL databases with considerations for security measures in Big Data deployments. The discussion forms a significant technical contribution for learners, application developers and Big Data deployers paving way for a better awareness and management of the NoSQL systems in an organization..
  • 关键词:NoSQL; Big Data; security; vulnerabilities; non;relational database; injection attacks
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