<![CDATA[The growing threat of web-based security attacks, particularly malware embedded in the form of scripts, has become a serious challenge on the Open Journal Systems (OJS) platform. Modern malware tends to be covert and difficult to detect using conventional static signature-based methods. This study aims to develop and evaluate a malware detection system based on script analysis on the front-end side of OJS. The method employed is an experimental approach with system engineering that encompasses web page crawling, HTML and JavaScript extraction, and script pattern analysis to detect malware indicators such as obfuscation, hidden scripts, and cloaking. The results show that the system was capable of conducting 393 scans across 241 OJS websites with a total of 60,396 pages analyzed, of which 11.94% of pages were indicated to contain malware. The system also proved capable of detecting hidden scripts and cloaking techniques through a comparison of crawling results obtained under different parameters. The conclusions of this study indicate that a client-side script analysis-based malware detection approach is effective in identifying hidden threats without requiring server access. This research contributes to the development of more adaptive malware detection methods as well as practical solutions for enhancing the security of the OJS platform.]]>
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