A Novel Machine Learning Approach for Database Exploitation to Enhance Database Security: A Survey

Abstract

As organizations increase their reliance on, possibly distributed, information systems for daily business, they become more vulnerable to security breaches even as they gain productivity and efficiency advantages. Though a number of techniques, such as encryption and electronic signatures, are currently available to protect data when transmitted across sites, a truly comprehensive approach for data protection must also include mechanisms for enforcing access control policies based on data contents, subject qualifications and characteristics, and other relevant contextual information, such as time. It is well understood today that the semantics of data must be taken into account in order to specify effective access control policies. Here this paper discusses the Machine learning-based approaches that can be implemented to improve the level of security in a database with emphasis on the discrepancies in security models that are composed of both traditional and advanced protection layers extending from input and output interfaces of databases and framework of databases. With the ever-increasing growth of online trading, it is possible to see how SQLi attacks can continue to be one of the leading routes for cyber-attacks in the future, as indicated by findings reported in OWASP It also proposes a combined architecture consisting of intricate cryptographic protocols, advanced anomaly detection systems and affordable access control solutions. As a result of the formation of efficient database ecosystems, this work underlines the need for the multiple-level approach by considering the organizational success factors and technical solutions. The Comprehensive approach in the paper benefits the numerous professionals and researchers who are endeavoring to protect databases against new emerging risks.

Keywords

Database safety, Security Oddity Detection, Authorization, Security hierarchies, Schemes of Security, Cyber Threats, Data Consistency, Artificial Intelligence, Machine Learning, Access Control, Cloud Database