AI recommendation penetration testing tool for SQL injection: linear regression - Ahmad Fuad, Norshahira Elliyana; Saad, Shahadan (College of Computing, Informatics, and Mathematics, 2025)
This project addresses to the urgent need for enhanced security assessments by incorporate artificial intelligence (AI) into penetration testing. This approach is essential because of the evolving landscape of cyber threats and the continuous advancement of technology. The issue statement highlight the importance of updating security measures to avoid the latest threats and vulnerabilities. Based on this, the research employ a linear regression technique in the AI penetration testing. This technique involves the development and implementation of an algorithm designed to improve the efficiency of security evaluations by suggest the suitable penetration testing tools. The important of this study lies in its response to the labor and time-consuming nature of current testing methods. The project objective is to apply an AI linear regression algorithm to improve not only the efficiency and flexibility of penetration testing but also to provide strong protection against many cybersecurity threats by suggesting the suitable tools. By suggesting the best tools, this approach reduces the manual effort required, and reduce timeconsuming to choose the suitable tools, allowing security professionals to focus on more complex tasks. This project, Extreme programming methodology was used to construct the flow of the project. To make sure this project going smoothly many hardware and software were used, such as Django, Nginx, VMware and many more. In this project, many tests were carried out such as unit testing and integration testing. While doing this project, several limitation were found such as the low accuracy of the tools suggestion, the installation problem and the result that not detail. This limitation can be improve in future works where they will be many advance technology in the future. In conclusion, the objective of this project is success because the linear regression algorithm was able to be insert in the penetration testing framework.