SQL injection (SQLi) is one of the most common attacks against database Servers and has the potential to threaten Server services by utilizing SQL commands to change, delete, or falsify data. In this study, researchers tested SQLi attacks against Websites using a number of tools, including Whois, SSL Scan, Nmap, Open Web Application Security Project (OWASP) Zap, and SQL Map. Then, researchers identified SQLi vulnerabilities on the tested Web Server. Next, researchers developed and implemented mitigation measures to protect the Website from SQLi attacks. Test results using OWASP Zap identified 14 vulnerabilities, with five of them at a medium level of 35%, seven at a low level of 50%, and two at an informational level of 14%. Meanwhile, testing using SQL Map succeeded in gaining access to the database and username on the Web Server. The next step in this research is to provide recommendations for installing a firewall on the Website as a mitigation measure to reduce the risk of SQLi attacks. The main contribution of this research is the development of a structured methodology to identify and address SQLi vulnerabilities in Web Servers, which play an important role in maintaining data security and integrity in a rapidly evolving online environment.

There are many opportunities to improve E-Learning Web-based applications with regard to continue challenges e.g. their lack of interaction between teachers and students and structural evaluation of the presented learning activity. In this paper, E-Learning system Web Server log data and information about its control panel are provided; then the gathered data were preprocessed. After extracting association rules form these data and selecting results using more confidence coefficients, they were used as a virtual consultant for improving the E-Learning system. MySQL was used as the database for storing and extracting patterns. Extracting association rules with more confidence coefficients was done using Weka Software. The selected E-Learning Web application was Moodle and the virtual university case study was Iran University of Science and Technology. The obtained results showed the needs which cannot be granted by a human consultant easily and cannot be inferred from current application log directly.