Application of machine learning and artificial intelligence in detecting SQL injection attacks - Md Sultan, Abu Bakar; Agiliga, Nwabudike Augustine; Osman, Mohd Hafeez Bin; Sharif, Khaironi Yatim (Indonesian Society for Knowledge and Human Development, 2024)
More recently, cyber-attacks have also been on the rise and SQL injection attacks are some of major threats to data security. AI and machine learning have come a long way, however their usage in cybersecurity is still somewhat nascent. The main aim of this work is focusing on solving the IT-related challenge lack-of-adequate knowledge bases and tools for security practitioners to monitor and mitigate SQL Injection attacks with AI/ML techniques. The study uses a mixed-methods approach to evaluate how well different AI and ML algorithms identify SQL injection attacks by combining algorithmic evaluation with empirical investigation. Datasets of well-known SQL injection attack patterns and AI/ML models intended for cybersecurity anomaly detection are among the resources underexplored, these findings show the potential for boosting detection capabilities by deploying ML and AI-based security solutions, with some algorithms scoring up to an 80 percent success rate in identifying SQL injections. But while the tool usage seems to be effective three-quarters of survey respondents reported less bad stuff getting through, with a similar number able to get more done in less time as security researchers or incident response practitioners on top of that adoption among cybersecurity pros was below 30%, demonstrating an opportunity gap between what could leverage and folks actually using it. This will help lay a groundwork for future work in terms of identifying the best solutions and providing potential approaches to incorporating AI/ML into cybersecurity frameworks. The implications of this study indicate that the adoption robust defenses against SQL injection and other cyber threats could increase many folds if we can continue to research and implement AI ML technologies.