Detecting Vulnerabilities in Senayan Library Management System Using Machine Learning

Vulnerabilities in the open-source Senayan Library Management System (SLiMS) can impact thousands of libraries globally.However, manual code reviews to uncover undisclosed weaknesses are time-consuming.This study proposes an automated machine learning based static analysis approach to detect vulnerabilities in SLiMS PHP code.A dataset of 22,877 samples with cross-site scripting, SQL injection and other weakness types is used to train classification models.Feature engineering extracted discriminative attributes indicating flaws while vectorization structured the code samples.A Multinomial Naï ve Bayes model achieved 97% accuracy in detecting security issues.Three critical vulnerabilities were identified in SLiMS v9.6.1 which were responsibly disclosed and fixed.The findings demonstrate feasibility of applying machine learning to boost open-source security.Further enhancements through custom datasets and deep neural networks can aid real-time vulnerability discovery.