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هفتمین کنفرانس ملی و اولین کنفرانس بین المللی محاسبات توزیعی و پردازش داده های بزرگ
Cross-site Scripting Attack Detection using Combination of Multi-Layer Perceptron and Naive Bayes Algorithms
Cross-site Scripting Attack Detection using Combination of Multi-Layer Perceptron and Naive Bayes Algorithms
نویسندگان :
Bahman Arasteh ( Istinye University ) , Behzad Amirfallahi ( دانشگاه آزاد تبریز ) , Keyvan Arasteh ( Istinye University ) , Giti Valizaddeh ( دانشگاه آزاد تبریز )
کلید واژه ها :
Software Security،XSS attacks،Multi-Layer Perceptron،Naive Bayes Algorithm،Accuracy
چکیده مقاله :
Today, the World Wide Web is the most widely used, cheapest, and fastest communication medium on the planet. Because of its accessibility, millions of individuals use it for their daily tasks. A website vulnerability is a fault or misconfiguration in a website's that allows an attacker to seize control of the site and maybe the hosting server. According to studies published in 2021 by the Open Web Applications Security Project, XSS attacks are a substantial danger to web applications and the XSS vulnerability ranking third among the top ten vulnerabilities of web applications. To detect XSS attacks, a combination of two multilayer perceptron algorithms and a Naive Bayesian method was utilized in this research. Based on data derived from URLs and JavaScript code, the proposed method employs a hybrid machine learning algorithm to categorize regular and malicious web pages. Regarding the results of conducted experiments on a real-world data set, a combination of Naive Bayesian and multilayer perceptron algorithms had higher accuracy and precision than the other similar algorithms.
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