Abstract:
Injection attacks, such as SQL injection and cross-site scripting (XSS), continue to pose significant security threats to web applications. These attacks exploit vulnerabilities in user input handling mechanisms to execute malicious code or gain unauthorized access to sensitive data. Detecting and mitigating injection attacks is a critical aspect of web application security.
This abstract presents an approach for the automatic detection of injection attacks in HTTP requests. Traditional methods of detecting injection attacks rely on a combination of manual code review, pattern matching, and the use of security rules. However, these methods often suffer from limitations such as high false positive rates, limited coverage of attack vectors, and the need for manual intervention.
The proposed approach leverages machine learning techniques to automatically detect injection attacks in HTTP requests. By training a model on a large dataset of labeled HTTP requests, the system can learn to identify patterns and characteristics associated with injection attacks. The model can then be used to analyze incoming HTTP requests in real-time, flagging those that exhibit suspicious behavior indicative of injection attacks.
The system incorporates a variety of features extracted from HTTP requests, including request headers, parameters, and payload content. These features are used to train a machine learning model, such as a recurrent neural network (RNN) or a random forest classifier, to classify requests as either benign or malicious.
To evaluate the effectiveness of the proposed approach, a comprehensive dataset comprising both benign and injection attack requests is used. The system is then benchmarked against traditional methods to assess its accuracy, detection rate, and false positive rate. The experimental results demonstrate the efficacy of the automated detection system, showcasing its ability to accurately identify injection attacks with minimal false positives.
The automatic detection of injection attacks in HTTP requests has several practical implications. It enables web application developers and security professionals to proactively identify potential security vulnerabilities and take appropriate mitigation measures. By reducing the reliance on manual inspection and rule-based systems, the proposed approach improves detection accuracy and efficiency, ultimately enhancing the security posture of web applications.
Keywords: Injection attacks, HTTP requests, web application security, machine learning, detection, mitigation.
AUTOMATIC DETECTION OF INJECTON ATTACK IN HTTP REQUESTS, GET MOREÂ Â COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS
