ABSTRACT
Malicious user requests pose a vicious threat to backend devices which execute them; more so, could result in the compromise of other user accounts, exposing them to theft and blackmail. It becomes imperative to sanitize such requests before they are treated by the servers as access to a single malicious request is enough to cause a disaster. A number of authors suggest that sanitizing models built on support vector machines guarantee optimum classification of malicious from non-malicious requests. In this work, we have been able to establish that the use of ensemble learner provides a better performance, especially when associated with a strong classifying tool like decision tree.
IBRAHIM, S (2021). Automatic Detection Of Injecton Attack In HTTP Requests. Afribary. Retrieved from https://afribary.com/works/automatic-detection-of-injecton-attack-in-http-requests
IBRAHIM, SODIQ "Automatic Detection Of Injecton Attack In HTTP Requests" Afribary. Afribary, 16 Apr. 2021, https://afribary.com/works/automatic-detection-of-injecton-attack-in-http-requests. Accessed 04 May. 2024.
IBRAHIM, SODIQ . "Automatic Detection Of Injecton Attack In HTTP Requests". Afribary, Afribary, 16 Apr. 2021. Web. 04 May. 2024. < https://afribary.com/works/automatic-detection-of-injecton-attack-in-http-requests >.
IBRAHIM, SODIQ . "Automatic Detection Of Injecton Attack In HTTP Requests" Afribary (2021). Accessed May 04, 2024. https://afribary.com/works/automatic-detection-of-injecton-attack-in-http-requests