Sankar

Authentication that
thinks like you do

A behavioural authentication system combining gesture input, representation-level drift detection, and adaptive trust control for continuous identity verification.

auth.log
// anomaly score
score: 0.03
status: "verified"
method: "gesture"
risk: LOW

Problem

Authentication systems are static — once verified, users are trusted indefinitely. This makes them vulnerable to session hijacking, replay attacks, and behavioural anomalies.

Solution

SecuADR introduces continuous authentication by monitoring behavioural patterns such as gesture dynamics. Using RLFS and S-ADR, the system dynamically evaluates trust instead of relying on a single login event.

System Architecture

INPUT

Gesture Signal

User interaction patterns

+ expand

REPRESENTATION

RLFS Layer

Detects behavioural drift

+ expand

CONTROL

S-ADR Engine

Adaptive trust response

+ expand

OUTPUT

Auth Decision

Allow, degrade, or reject

+ expand

Why this matters

Security systems should adapt to user behaviour in real time. SecuADR shifts authentication from a binary gate into a continuous, intelligent process.

Key Features

Gesture-based fallback authentication
Continuous behavioural verification
Adaptive trust scoring
Drift-aware security response

Build Journey

Idea

Rethought authentication as continuous process

Prototype

Built gesture-based login system

AI Layer

Integrated RLFS + S-ADR

System Design

Expanded into full auth framework

Tech Stack

React NativeNode.jsMongoDB

Related Systems

hidden layer

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