A privacy-preserving authentication system that detects account hijacking from contextual login behavior.
Login context — IP, device fingerprint, browser, location and time — is analysed for anomalies, with detection models trained across devices through federated learning so raw user data never leaves the client. SVM and Logistic Regression assign each login a risk level, and Zero Trust adaptive authentication enforces context-aware challenges based on that risk.
Stolen credentials defeat password authentication entirely, and the behavioural data needed to spot a hijacked login is exactly the data users least want centralised on a server.
Train the anomaly detection models federated across devices so raw login context stays local, and use the resulting risk score to drive Zero Trust adaptive challenges rather than a fixed rule.
Contextual Anomaly Detection
Analyzes IP, device fingerprint, browser, location, and login time.
Federated Learning
Trains detection models across devices without sharing raw user data.
Risk-Scored Login Classification
SVM and Logistic Regression assign a login risk level.
Zero Trust Adaptive Authentication
Enforces context-aware security challenges based on risk.
Machine Learning
Security
Application
Context capture
IP, device, browser, location and time are collected at login.
Local training
Models train on-device without exporting raw data.
Federated aggregation
Model updates are aggregated centrally.
Risk scoring
Each login attempt is assigned a risk level.
Adaptive challenge
Zero Trust rules enforce challenges proportional to risk.
Every project is quoted individually, because the price depends on the modules you need, your technology stack, your college's format and your deadline. Send us the project name on WhatsApp and we'll share a quote the same day.
Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.
That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.
We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.
Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.
Get complete source code, documentation, PPT, explanation, and support.
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