SQL-CB-GuArd: a deep learning mechanism for structured query language injection attack detection

Abstract

Structured query language (SQL) injection attacks, which take advantage of input field vulnerabilities to introduce malicious code into database queries, are a serious danger to database-driven programs and systems. Intruders can now alter, recover, or remove sensitive data because of illegal access. Strong artificial intelligence (AI) based security solutions are required to reduce SQL injection threats, as these assaults' significance highlights. This study's main goal is to create automated AI-based techniques that can identify structured query language injection attack (SQLIA) in real time eliminating the need for human intervention. Although machine learning (ML) and deep learning-based techniques have received a lot of interest in this field, MLbased techniques have problems with accuracy and false negatives. Deep learning (DL) is therefore commonly used in these text data processing and natural language processing (NLP) applications. We have introduced a hybrid DL approach for SQLIA detection in this paper. The pre-processing step performs decoding, generalization, and tokenization to improve the learning performance. The proposed approach uses combination of convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU) with attention mechanism. The combination helps to improve the pattern learning capacity. The proposed approach is validated on publically available data and experimental analysis reported that the proposed SQL-CB-GuArd achieves better accuracy of SQLIA detection.