Machine Learning-Based Account Takeover Detection System Using Behavioral Login Analysis and Risk-Adaptive Authentication

Account Takeover (ATO) is a major cybersecurity threat in which attackers gain unauthorized access to legitimate user accounts. This paper proposes a Machine Learning-Based Account Takeover Detection System that analyzes login activities such as IP address, device details, location, login time and failed login attempts to identify suspicious behavior. Logistic Regression and Random Forest algorithms are used to predict login risk and to generate a risk score between 0 and 1. Based on the risk level, the system takes one of three actions: Allow, Multi-Factor Authentication (MFA) or Block. The trained model is served through a FastAPI backend for real-time prediction, a Streamlit interface lets users submit login attributes and view the decision, and every prediction is logged in an SQLite database. The proposed system aims to improve account security, detect suspicious login attempts and prevent unauthorized access