Security in database management system using machine learning

The term 'database security' refers to the collection of rules, tools, and processes that have been developed to maintain and protect the databases' confidentiality, integrity, and accessibility. The use of machine learning to improve database management security is becoming more common. The fundamental goal of employing machine learning in security is to make the process of malware detection more actionable, scalable, and successful than conventional techniques, which need the participation of humans. This may be accomplished by making the process more automated. The process entails overcoming problems posed by machine learning, which need to be managed in an effective, logical, and theoretical manner. Machine learning algorithm is applied in the critical paths of the tuner. The optimum configuration for the proposed system yields a throughput boost of between 22% and 35% and a latency reduction of around 60%. The method is robust to various attacks.