Credential Stuffing Attack Detection Using AI&ML

#cybersecurity #ai #machinelearning #security

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 processes authentication signals and integrates with a Streamlit-based security dashboard that visualizes threat intelligence and attack probability. The proposed system demonstrates how behavioral anomaly detection can improve authentication security and prevent account takeover attacks in modern web applications.

Keywords

Cybersecurity, Credential Stuffing, Anomaly Detection, Machine Learning, Authentication Security, FastAPI, Streamlit

Introduction

With the increasing number of data breaches worldwide, attackers frequently use leaked username-password combinations to perform credential stuffing attacks on web applications. These attacks rely on automated scripts to test thousands of credentials across authentication systems. Traditional security solutions often rely on static rules, which are insufficient to detect advanced automated attacks.

Credential stuffing attacks are particularly dangerous because they use valid credentials obtained from previous breaches. This makes detection challenging, as the login attempts may appear legitimate.

This research presents an AI-based behavioral analysis system that detects credential stuffing attacks by analyzing authentication patterns such as login velocity, failure ratios, device concurrency, and bot detection scores. The system integrates machine learning models with rule-based threat classification to provide real-time detection and mitigation capabilities.

Problem Statement

Modern authentication systems face increasing threats from automated credential stuffing attacks. These attacks use bots to test large numbers of credentials rapidly, leading to unauthorized account access and data breaches. Existing solutions such as rate limiting and CAPTCHA are often bypassed by sophisticated automation tools. Therefore, there is a need for an intelligent detection system capable of analyzing authentication behavior and identifying suspicious login patterns automatically.

Proposed System

The proposed system introduces a real-time credential stuffing detection framework based on behavioral anomaly detection. The system architecture consists of four main components:

Feature Engineering Layer
Extracts behavioral login signals such as login velocity, failure ratio, bot detection score, and geolocation anomalies.

Machine Learning Detection Model
Uses an Isolation Forest anomaly detection algorithm to identify abnormal login behavior.

Risk Scoring Engine
Applies rule-based thresholds to classify login attempts as LOW, MEDIUM, or HIGH risk.

Security Dashboard and API Layer
A FastAPI backend processes login events, while a Streamlit dashboard visualizes risk metrics and attack analytics.

System Architecture

System pipeline:

Login Attempt
→ Feature Extraction
→ Machine Learning Model (Isolation Forest)
→ Risk Scoring Engine
→ Security Action (Allow / OTP / Block)
→ Visualization Dashboard

This architecture enables real-time threat detection and monitoring of authentication activities.

Methodology Feature Engineering

Authentication logs are processed to extract behavioral indicators such as:

Failed login ratio

Bot detection score

Geolocation distance between logins

Concurrent device attempts

Machine Learning Model

The Isolation Forest algorithm is used for anomaly detection. This model isolates abnormal observations in the dataset, allowing the system to detect suspicious login patterns without requiring labeled attack data.

Risk Classification

A rule-based risk engine evaluates login behavior based on predefined thresholds and assigns a risk level.

Implementation

The system is implemented using the following technologies:

Component Technology
Backend API FastAPI
Machine Learning Scikit-learn
Dashboard Streamlit
Data Processing Pandas
Visualization Plotly

The machine learning model is trained using authentication behavior datasets and deployed using a FastAPI service for real-time predictions.

Results

The system successfully detects abnormal login patterns associated with credential stuffing attacks. By combining anomaly detection with rule-based risk scoring, the system can classify login attempts and trigger automated mitigation strategies such as blocking login attempts or requiring OTP verification.

Conclusion

Credential stuffing attacks continue to pose significant security risks for web applications. The proposed AI-based detection system demonstrates how behavioral anomaly detection can improve authentication security and detect automated attacks in real time. Future improvements may include integrating threat intelligence feeds, advanced bot detection mechanisms, and deep learning-based behavioral models.