Malicious Query Recognition Using Chosen Machine Learning Techniques

Abstract The successful development of an effective machine learning model for detecting malicious queries is a challenging task that requires domain expertise. Malicious queries are complex and constantly evolving, and their detection is further complicated by variations in database server architectures. To tackle this challenge, domain experts with a deep understanding of attack vectors, techniques, and trends are crucial. Their insights into the patterns, behaviour, and characteristics of malicious queries provide invaluable guidance for designing appropriate features and training the model. This study aims to leverage this domain expertise to propose a robust machine-learning model capable of accurately identifying and categorizing malicious queries. By incorporating expert knowledge and using advanced techniques like machine learning, the model can be optimized to address the dynamic nature of these threats, leading to improved accuracy in real-world scenarios. After the extensive experiment, using 88,213 malicious and normal queries from GitHub and other sources, the Random Forest model yielded the highest accuracy of 98.4% and 97% sensitivity compared to other machine learning models experimented in the study. The performance comparison of the study with existing works indicates that the machine learning model achieved promising results.