Abstract
This paper presents a practical approach to training an AI model to detect injection attacks, focusing on the creation of a manufactured dataset via structured hands-on methods. By establishing a vulnerable web server using XAMPP and DVWA (Damn Vulnerable Web Application), the research aims to simulate various injection attacks and capture relevant network traffic data. The paper discusses the methodology of data collection, AI model development, and performance evaluation.
Document Type
Paper
Disciplines
Digital Communications and Networking | Other Computer Engineering
DOI
10.25776/htag-sg82
Publication Date
11-11-2024
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Training an AI to Detect Injection Attacks Using a Hands On Approach
This paper presents a practical approach to training an AI model to detect injection attacks, focusing on the creation of a manufactured dataset via structured hands-on methods. By establishing a vulnerable web server using XAMPP and DVWA (Damn Vulnerable Web Application), the research aims to simulate various injection attacks and capture relevant network traffic data. The paper discusses the methodology of data collection, AI model development, and performance evaluation.
Comments
1.“Damn Vulnerable Web Application (DVWA)” https://dvwa.co.uk/ 2.“Wireshark” https://www.wireshark.org/ 3.“XAMPP” https://www.apachefriends.org/ 4.“Python” https://www.python.org/ 5.Vscode was used as the IDE in this project: https://code.visualstudio.com/