Published Paper Details:

Users' crucial data is stored in databases by the web-based applications that collect it. Due to their Internet accessibility, these applications and the databases that are connected to are exposed to a variety of information security risks. The threats include cyber attacks like Cross Site Scripting (CSS), Denial of Service Attack (DoS), and SQL Injection Attacks. When considering web-based applications, the top ten vulnerabilities includes SQL Injection attacks. An organization or corporation may suffer harm as a consequence of this kind of attack since the attacker can obtain or steal sensitive information. In order to classifying and recognize SQL Injection attacks, we are considering utilizing an algorithm from machine learning approaches. Any web system must include a robust SQL injection detection to counteract this risk. In this project, machine learning is applied to provide using training data, which is then analyzed against testing data. The outcomes of the detection demonstrates that the proposed approach generates high accuracy in differentiating between malicious and harmless web requests.