SQL Injection Detection Based on Lightweight Multi-Head Self-Attention - Rui-Teng Lo; Wen-Jyi Hwang; Tsung-Ming Tai (Applied Sciences)
This paper presents a novel neural network model for the detection of Structured Query Language (SQL) injection attacks for web applications. The model features high detection accuracy, fast inference speed, and low weight size. The model is based on a novel Natural Language Processing (NLP) technique, where a tokenizer for converting SQL queries into tokens is adopted as a pre-processing stage for detection. Only SQL keywords and symbols are considered as tokens for removing noisy information from input queries. Moreover, semantic labels are assigned to tokens for highlighting malicious intentions. For the exploration of correlation among the tokens, a lightweight multi-head self-attention scheme with a position encoder is employed. Experimental results show that the proposed algorithm has high detection performance for SQL injection. In addition, compared to its lightweight NLP counterparts based on self-attention, the proposed algorithm has the lowest weight size and highest inference speed. It consumes only limited computation and storage overhead for web services. In addition, it can even be deployed in the edge devices with low computation capacity for online detection. The proposed algorithm therefore is an effective low-cost solution for SQL injection detection.