Cross-site Scripting Attack Detection Using Machine Learning with Hybrid Features

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Dimaz Arno Prasetio
Kusrini Kusrini
M. Rudyanto Arief


This study aims to measure the classification accuracy of XSS attacks by using a combination of two methods of determining feature characteristics, namely using linguistic computation and feature selection. XSS attacks have a certain pattern in their character arrangement, this can be studied by learners using n-gram modeling, but in certain cases XSS characteristics can contain a certain meta and synthetic this can be learned using feature selection modeling. From the results of this research, hybrid feature modeling gives good accuracy with an accuracy value of 99.87%, it is better than previous studies which the average is still below 99%, this study also tries to analyze the false positive rate considering that the false positive rate in attack detection is very influential for the convenience of the information security team, with the modeling proposed, the false positive rate is very small, namely 0.039%


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How to Cite
D. Prasetio, K. Kusrini, and M. R. Arief, “Cross-site Scripting Attack Detection Using Machine Learning with Hybrid Features”, J. INFOTEL, vol. 13, no. 1, Feb. 2021.