A deep learning approach based on multi-view consensus for SQL injection detection

Abstract

SQL injection (SQLi) attacks are one of the oldest and most serious security threats, consistently ranking among the top ten critical web security risks. Traditional defense mechanisms against SQL injection predominantly use blacklists to disallow common injection characters or terms. However, the major challenge for these systems is to create a comprehensive list of potential SQLi characters, terms, and multi-terms that encompass various types of SQLi attacks (time-based, error-based, etc.), taking into account various SQL datasets (such as MySQL, Oracle, and NoSQL). Recently, some research studies have concentrated on feature learning from SQL queries by applying some well-known deep architectures to detect SQLi attacks. Motivated by a similar objective, this research introduces a novel deep learning-based SQLi detection system named Bidirectional LSTM-CNN based on Multi-View Consensus (MVC-BiCNN). The proposed method implements a pre-processing step that generates multiple views from SQL data by semantically encoding SQL statements into their corresponding SQL tags. By utilizing two different main layers, which are bidirectional long short-term memory (LSTM) and convolutional neural network (CNN), the proposed method learns a joint latent space from multi-view representations. In the detection phase, the proposed method yields separate predictions for each representation and assesses whether the query constitutes an SQLi attack based on a consensus function's output. Moreover, Interpretable Model-Agnostic Annotations (LIME), one of the methods of Explainable Artificial Intelligence (XAI), is employed for the purpose of interpreting the model's results and analyzing the SQL injection (SQLi) inputs. The experimental results demonstrate that MVC-BiCNN outperforms the baseline methods, yielding 99.96% detection rate.