Seok, J.B. and Sung, B.C. (2017) A Hybrid System of Deep Learning and Learning Classifier System for Database Intrusion Detection. 2017 International Conference on Hybrid Artificial Intelligence Systems, La Rioja, 21-23 June 2017, 615-625.
https://doi.org/10.1007/978-3-319-59650-1_52

has been cited by the following article:

• TITLE: The Overview of Database Security Threats’ Solutions: Traditional and Machine Learning

  AUTHORS: Yong Wang, Jinsong Xi, Tong Cheng

  KEYWORDS: Database Security, Threat Agent, Traditional Approaches, Machine Learning

  JOURNAL NAME: Journal of Information Security, Vol.12 No.1, January 11, 2021

  ABSTRACT: As an information-rich collective, there are always some people who choose to take risks for some ulterior purpose and others are committed to finding ways to deal with database security threats. The purpose of database security research is to prevent the database from being illegally used or destroyed. This paper introduces the main literature in the field of database security research in recent years. First of all, we classify these papers, the classification criteria are the influencing factors of database security. Compared with the traditional and machine learning (ML) methods, some explanations of concepts are interspersed to make these methods easier to understand. Secondly, we find that the related research has achieved some gratifying results, but there are also some shortcomings, such as weak generalization, deviation from reality. Then, possible future work in this research is proposed. Finally, we summarize the main contribution.