Strategic Innovations in Network Security: A Machine Learning Approach to Detect and Mitigate Backdoor Attacks
Aadil Khan, Deepali Gupta, Monica Dutta · 2025
A backdoor attack is a malicious act in which a hacker takes advantage of holes in the system to enter without authorization. This requires changing the network and authentication processes in order to insert malicious code and provide an entry point for unwanted activities. Early detection of backdoor attacks is essential to preventing significant damage. Proactive defence searches for irregularities in network traffic before dubious behaviours become widespread. Prompt innovation boosts the robustness of a cybersecurity system, protecting information and infrastructure against illegal access and possible breaches. This paper emphasizes proactive security measures and offers a methodical approach to identifying backdoor attacks. Strong early detection might be achieved at the network-database interface with ensemble machine learning. This helps to ensure that SQL queries are corrected correctly and prevents unwanted access. Our work improves cybersecurity resilience by deepening our knowledge of how to successfully defend against backdoor attacks. Producing datasets for machine learning algorithms of superior quality is essential to the backdoor incursion detection procedure. The variety of conditions it offers makes accurate testing of detecting systems possible. The use of ensemble machine learning on datasets like UNSW-NB15 improves the accuracy of backdoor attack detection, hence supporting the development of an all-encompassing and efficient cybersecurity plan.