Combining Regular Expressions and Machine Learning for SQL Injection Detection in Urban Computing
Michael S. Souza, Silvio Ribeiro, Vanessa C. Lima, Francisco J. Cardoso, Rafael L. Gomes
Abstract
Given the vast amount of data generated in urban environments the rapid advancements in information technology urban environments and the continual advancements in information technology, several online urban services have emerged in recent years. These services employ relational databases to store the collected data, thereby making them vulnerable to potential threats, including SQL Injection (SQLi) attacks. Hence, there is a demand for security solutions that improve detection efficiency and satisfy the response time and scalability requirements of this detection process. Based on this existing demand, this article proposes an SQLi detection solution that combines Regular Expressions (RegEx) and Machine Learning (ML), called Two Layer approach of SQLi Detection (2LD-SQLi). The RegEx acts as a first layer of filtering for protection against SQLi inputs, improving the response time of 2LD-SQLi through RegEx filtering. From this filtering, it is analyzed by an ML model to detect SQLi, increasing the accuracy. Experiments, using a real dataset, suggest that 2LD-SQLi is suitable for detecting SQLi while meeting the efficiency and scalability issues.