A Large Language Model Based SQL Injection Attack Detection Method

doi:10.3969/j.issn.1671-1122.2023.11.009

Abstract

Abstract:

The SQL injection attack, widely employed by attackers, poses a significant threat to cyberspace security. Traditional detection methods for SQL injection attacks include rule-based and machine learning-based method, suffering from limited applicability and high false positive rates. This paper proposed a large language model-based method for detecting SQL injection attacks. By applying prompt engineering and instruction fine-tuning techniques, a specialized large language model for SQL injection attack detection was developed; Additionally, the impact of iteration rounds, the number of fine-tuning samples and inference parameters on model performance was analyzed to enhance the detection capability of large language models; Leveraging the robust semantic understanding capability of the large language model significantly reduced the false positive rate. This paper conduct experimental analysis on a specialized large language model for SQL injection attack detection that we proposed, using the Kaggle dataset. The model achievedes an accuracy rate of over 99.85%, a false alarm rate of less than 0.2%, and an F1 score of 0.999. Compared to the current state-of-the-art methods for SQL injection attack detection, our model demonstrates a significant improvement in detection performance.

Key words: SQL injection attack, attack detection, large language model, prompt engineering, instruction tuning

CLC Number:

TP309

HUANG Kaijie, WANG Jian, CHEN Jiongyi. A Large Language Model Based SQL Injection Attack Detection Method[J]. Netinfo Security, 2023, 23(11): 84-93.

Figures/Tables 13

References 36

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关键字	映射标签
Yes, True, 1	SQL注入攻击类
No, False, 0	非SQL注入攻击类
其他	未知

样本类别	训练集/条	测试集/条	验证集/条
SQL注入攻击样本	8000	2000	200
非SQL注入攻击样本	8000	2000	200

模型	迭代轮数/轮	微调样本数/个	Temperature	Top P
SQLLM-7B	134400	16000	1.0	0.75
SQLLM-13B	134400	16000	0.4	0.75

方法	准确率	误报率	F1值
文献[11]方法	99.30%	0.70%	0.993
文献[12]方法	98.00%	未知	0.989
文献[22]方法	96.00%	3.84%	0.947
文献[23]方法	98.30%	2.29%	0.970
SQLLM-7B	99.85%	0.10%	0.999
SQLLM-13B	99.90%	0.20%	0.999

方法	模型	单个样本时间开销/ms
文献[13]方法	MLP	74.00
文献[35]方法	随机森林	2.94
文献[36]方法	Word2vec+KNN	13.30
SQLLM-7B	LLaMA2-7B	176.10
SQLLM-13B	LLaMA2-13B	295.60