Published Paper Details:

The increased proliferation of online apps and services has raised serious concerns about the possibility of cyberattacks. SQL injection is a common attack type that takes advantage of holes in online applications to access databases without authorization. Maintaining the integrity and security of online systems depends on identifying and thwarting SQL injection attacks. In this study, we use the Sequential Minimal Optimization (SMO) algorithm to present a unique method for identifying SQL injection attacks in network traffic data. This study proposes a unique strategy that leverages machine learning to solve the critical requirement for efficient and effective detection procedures. This study specifically focuses on using the (SMO) technique to identify and mitigate SQL injection threats using network traffic data. The sequence of contacts between hosts, or network flow data, provides a wealth of information for identifying unusual patterns suggestive of attack activity.