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- W2893362671 abstract "Cross-Site Scripting (XSS) is one of the most popular attacks targeting web applications. Using XSS attackers can obtain sensitive information or obtain unauthorized privileges. This motivates building a system that can recognise a malicious script when the attacker attempts to store it on a server, preventing the XSS attack. This work uses machine learning to power such a system. The system is based on a combination of classifiers, using cascading to build a two phase classifier and the stacking ensemble technique to improve accuracy. The system is evaluated and shown to achieve high accuracy and high detection rate on a large real world dataset." @default.
- W2893362671 created "2018-10-05" @default.
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- W2893362671 date "2018-01-01" @default.
- W2893362671 modified "2023-09-27" @default.
- W2893362671 title "Preventing Cross-Site Scripting Attacks by Combining Classifiers" @default.
- W2893362671 doi "https://doi.org/10.5220/0006894901350143" @default.
- W2893362671 hasPublicationYear "2018" @default.
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