Credential Stuffing Attack Detection Using AI&ML

AI-Based Credential Stuffing Attack Detection Using Behavioral Anomaly Analysis Author: Ashwin Chauhan B.Tech Computer Science Engineering Prashanti Institute of Technology and Science, Ujjain Abstract Credential stuffing attacks have become one of the most common threats to online authentication systems, enabling attackers to gain unauthorized access to user accounts using previously leaked credentials. Traditional security mechanisms such as CAPTCHA and password policies often fail to detect automated login attempts effectively. This paper proposes an AI-based credential stuffing detection framework that analyzes behavioral authentication patterns to identify suspicious login activities in real time. The system utilizes machine learning techniques, specifically an Isolation Forest anomaly detection model, combined with rule-based risk scoring to detect abnormal login behaviors such as high login velocity, high failure ratios, and bot-like interaction patterns. A FastAPI backend proce