Code Injection attacks such as SQL Injection and Cross-Site Scripting (XSS) are among the major threats for today's web applications and systems. This paper proposes CODDLE, a deep learning-based intrusion detection systems against web-based code injection attacks. CODDLE's main novelty consists in adopting a Convolutional Deep Neural Network and in improving its effectiveness via a tailored pre-processing stage which encodes SQL/XSS-related symbols into type/value pairs. Numerical experiments performed on real-world datasets for both SQL and XSS attacks show that, with an identical training and with a same neural network shape, CODDLE's type/value encoding improves the detection rate from a baseline of about 75% up to 95% accuracy, 99% precision, and a 92% recall value.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.