AI-Powered Detection and Mitigation of Backdoor Attacks on Databases Server

A malicious act known as a backdoor attack occurs when an attacker takes advantage of system weaknesses in order to obtain unauthorized access. This requires modifying the network and the authentication protocols in order to implant malicious code, so establishing an access point for activities that are not permitted. It is important to do early detection of backdoor attacks in order to avert significant damage. A proactive defense involves the detection of abnormalities in the behavior of the network before the progression of suspicious operations. Timely innovation improves the resilience of a cybersecurity system, therefore protecting systems and data against illegal access and the possibility of breaches. This research study offers insightful methodology on the detection of backdoor attacks, with a focus on preventative security measures. Strong early detection can be achieved by using ensemble machine learning at the network-database interface. This helps to prevent unwanted access and rectify SQL queries faultlessly. This work strengthens the resiliency of cybersecurity by adding to a complete knowledge of combating backdoor attacks for effective threat management. To develop good Machine learning algorithm dataset is an essential component and in the process of discovering backdoor intrusions. Becaus e of the variety of situations it presents, accurate testing of detection technologies is made possible. The use of ensemble machine learning on datasets like UNSW-NB15 improves the accuracy of backdoor attack detection, which contributes to the development of a complete and efficient cybersecurity plan.