User Behaviour Authentication

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User Behaviour Authentication

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₹4,999.00

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₹4,999.00

Overview
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Detail Description

1. Abstract

User Behaviour Authentication is an AI-based security system that verifies a user's identity by analyzing their unique behavioural patterns. Unlike traditional authentication methods that rely only on passwords or one-time passwords, this system examines behavioural characteristics such as typing speed, mouse movements, keystroke dynamics, navigation patterns, and interaction habits. Machine learning algorithms analyze the collected behavioural data to identify legitimate users and detect unusual activity. When suspicious behaviour is detected, the system can trigger additional verification or restrict access based on configured security rules. This project aims to improve account security, reduce unauthorized access, and provide continuous user authentication for web applications and digital platforms.

2. Objectives

To develop an intelligent system for authenticating users through behavioural patterns.
To analyze keystroke dynamics, mouse movements, and interaction habits.
To apply machine learning algorithms for behavioural pattern recognition.
To detect suspicious activities and potential unauthorized access.
To support continuous authentication during active sessions.
To generate alerts when user behaviour deviates significantly from established patterns.
To improve application security while maintaining a convenient user experience.

3. Existing System

Traditional authentication systems primarily use passwords, PINs, OTPs, and biometric verification to confirm user identity. Although these methods provide basic protection, an account may remain vulnerable if login credentials are stolen or shared.

Disadvantages of the Existing System

Stolen passwords can be used to access user accounts.
Authentication is often performed only during login.
Traditional methods may not detect suspicious activity after login.
Password reuse and weak passwords increase security risks.
Additional verification can interrupt the user experience.
Fixed authentication rules may not adapt to changing user behaviour.

4. Proposed System

The proposed system uses machine learning to establish a behavioural profile for each user and compare current interactions with previously observed patterns. It collects permitted interaction data, extracts behavioural features, and evaluates whether the activity is consistent with the user's normal behaviour.

Key Features

Secure user registration and login.
Behavioural data collection, including typing patterns and mouse interactions.
Feature extraction and behavioural profile creation.
Machine learning-based user verification.
Anomaly detection for suspicious activity.
Risk scores and authentication status displayed through a dashboard.
Alerts or additional verification when unusual behaviour is detected.
Admin dashboard for monitoring authentication events and security alerts.

The system should collect only necessary behavioural data with appropriate user consent and protect it against unauthorized access.

5. Implementation Procedure

Requirement Analysis: Identify the application requirements, authentication workflow, and behavioural signals to be analyzed.
Dataset Collection: Collect consent-based behavioural interaction data or use a suitable research dataset.
Data Preprocessing: Remove invalid records, handle missing values, and normalize numerical features.
Feature Extraction: Calculate features such as typing intervals, typing speed, mouse movement patterns, click frequency, and session interaction statistics.
User Behaviour Profiling: Establish baseline behavioural patterns for each registered user.
Model Development: Train suitable algorithms such as Random Forest, Support Vector Machine (SVM), Isolation Forest, or other anomaly-detection models.
Authentication Logic: Compare current behavioural patterns against the user's profile and calculate an authentication or risk score.
Interface Development: Build the application using Flask or Django, with a user interface for login and an admin dashboard for security monitoring.
Testing and Evaluation: Evaluate false acceptance rates, false rejection rates, precision, recall, and detection performance using separate test data.
Deployment: Deploy the application in a secure environment and configure alerts, data retention, and additional verification for high-risk sessions.

6. Software Requirements

Programming Language: Python
Frontend: HTML, CSS, JavaScript, Bootstrap
Backend: Flask or Django
Machine Learning: Scikit-learn
Data Processing: NumPy, Pandas
Data Visualization: Matplotlib, Plotly
Database: MySQL, PostgreSQL, or SQLite
Development Environment: VS Code, Jupyter Notebook
Operating System: Windows or Linux

7. Hardware Requirements

Processor: Intel Core i5 or equivalent
RAM: Minimum 8 GB
Storage: 5–10 GB of available space, depending on dataset size
Input Devices: Keyboard and mouse or touchpad
Internet Connection: Required for web-based deployment and remote access
GPU: Not required for basic machine learning models

8. Advantages of the Project

Adds an additional layer of security beyond passwords.
Helps detect suspicious account activity after login.
Supports continuous authentication during user sessions.
Can identify behavioural anomalies using machine learning.
Reduces dependence on repeated manual authentication.
Supports security monitoring through dashboards and alerts.
Can be integrated into banking portals, enterprise applications, and web platforms.
Provides opportunities to improve detection as more representative behavioural data becomes available.

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