Impact Factor: 7.883
Submit Paper
Volume 12, Issue 4 (April 2026)

A Novel Steganographic Approach To Strengthen Enhanced Mfa And Attack Prevention For Credential Transmission

Impact Factor
7.883
Call For Paper
Volume 12 Issue 07

July 2026

Download Paper Format
Copyright Form
Share on:

Author(s)

Mrs. Banuppriya P Bharathiraja S Jeyachandran R Pradeep Raj S Rakesh R

Abstract

Digital Banking Infrastructure Faces Escalating And Sophisticated Threats Including Phishing, Man-in-the-Middle (MITM) Interceptions, Session Hijacking, Replay Attacks, Credential Stuffing, And Denial-of-service (DoS) Campaigns. Conventional Single-factor Authentication Mechanisms Based On Username-password Pairs Offer Insufficient Protection, While Existing Multi-Factor Authentication (MFA) Implementations—such As SMS-based One-Time Passwords (OTP), Hardware Tokens, And Basic Biometric Checks—continue To Exhibit Exploitable Vulnerabilities. This Paper Proposes A Novel Five-layer Secure Authentication And Transaction Authorization Framework Tailored To Digital Banking Environments. The System Integrates: (i) Grassmann Manifold-based Facial Recognition For Biometric Enrollment And Live Verification, Replacing Hardware USB Tokens With A Mathematically Robust Biometric Factor; (ii) Multi-factor Login Combining Credential-based Authentication With Biometric Matching; (iii) Dynamic Per-session Cryptographic Key Generation Using SHA-512 With User-specific Salts; (iv) QR-code Least Significant Bit (LSB) Steganography For Covert Session Key Transmission To The User's Registered Email, Hiding Sensitive Token Data Within An Innocuous Carrier Image; And (v) Per-transaction Session Key Validation With Real-time Unauthorized-access Alerting. The Proposed Architecture Extends And Improves Upon The Secure Multi-Factor Authentication (SMFA) Framework By Sarower Et Al. [1] By Eliminating Physical Device Dependency, Adding Biometric Security, And Introducing A Banking-domain-specific Steganographic Session Key Channel. Security Analysis Via Burrows-Abadi-Needham (BAN) Logic Demonstrates Protocol Correctness. The Facial Recognition Module Achieves A 97.3% True Acceptance Rate (TAR) With A False Acceptance Rate (FAR) Below 0.8%. Steganographic Embedding Achieves A PSNR Of 43.2 DB, Well Above The 40 DB Imperceptibility Threshold. Total Authentication Pipeline Latency Is Approximately 2.3 Seconds On Standard Hardware.


Keywords

Biometric Authentication Banking Security Grassmann Algorithm LSB Steganography Multi-factor Authentication QR Code Steganography Session Key Management SHA-512.

Paper ID

IJSARTV12I4105038

Publication Date

April 17, 2026

Research Area

Computer Science And Engineering

Submit Your Paper to IJSART

Join the global research community with IJSART. Submit your paper, share your work, and gain worldwide recognition!