Smart SQL Injection Attack Detection Using Hybrid Deep Neural Network

Abstract: SQL Injection attacks are one of the most critical and frequently occurring security threats in modern web applications. These attacks exploit vulnerabilities in input handling to inject malicious SQL statements, enabling attackers to bypass authentication, retrieve confidential data, modify database contents, or even gain complete control over backend systems. Traditional defense mechanisms such as manual input validation, rule-based filtering, and signature-based intrusion detection systems are limited in their ability to detect complex, obfuscated, and previously unseen attack patterns. This project proposes a Smart SQL Injection Attack Detection System using a Hybrid Deep Neural Network (CNN + LSTM) to effectively identify both known and unknown SQL injection attacks. Incoming SQL queries are preprocessed through tokenization and encoding, converted to numerical form using TF-IDF, and refined with Chi-Square feature selection. CNN layers capture local attack signatures while LSTM layers model sequential and contextual relationships among query tokens, jointly improving learning capability and detection accuracy. The model is trained and evaluated on benchmark and real-world SQL injection datasets using accuracy, precision, recall, and F1-score. Experimental results show detection accuracy consistently above 95%, with lower false positive rates than traditional and single-model detection techniques, and the system is suitable for real-time deployment in existing web application security infrastructures.

Keywords: SQL Injection, Cybersecurity, Deep Learning, Hybrid Neural Network, CNN, LSTM, Web Security, Attack Detection.