Tautology-Based Injection to Bypassing Authentication Detection Dataset

This dataset, titled Tautology-Based Injection to Bypassing Authentication Detection Dataset, has been developed to facilitate research and experimentation in the detection of SQL Injection (SQLi) attacks, specifically focusing on the tautology-based injection technique used to bypass authentication mechanisms. The dataset contains both malicious and benign SQL query instances, meticulously labeled to support supervised and unsupervised machine learning approaches. Malicious samples are crafted to represent a wide range of tautology-based attack patterns, including variations in syntax, case sensitivity, whitespace usage, and logical operator combinations, ensuring coverage of both simple and obfuscated attack forms. Benign queries are designed to reflect realistic authentication-related SQL statements in typical web applications. This dataset aims to serve as a benchmark resource for security researchers, penetration testers, and data scientists in evaluating and improving SQLi detection algorithms, intrusion prevention systems, and secure coding practices. It contributes to the advancement of cyber defense by enabling reproducible experiments and comparative analysis of detection methodologies.