🔔 Scheduled Maintenance:The platform will undergo backend updates on July 1st, 2026, 2:00–4:00 AM UTC.
AJETAS
Back to Journal Table of Contents
Paper Code: AJETAS-2026-1652Open AccessResearch ArticlePeer Reviewed

AI-based User Authentication Reinforcement by Continuous Extraction of Behavioral Interaction Features

More Rushikesh;vedant aher
Received: May 15, 2026Accepted: June 20, 2026Published: July 2026
Published inAvrmitra Journal of Engineering, Technology and Applied Sciences
Volume & IssueVol. 4, Issue 2
DOI10.2694/ajetas.2026.9859

Abstract & Keywords

Traditional authentication methods verify a user only at login and cannot detect account takeover after access has been granted. This paper proposes an AI-based continuous authentication system that strengthens security by continuously analyzing behavioral interaction features, particularly mouse movement patterns. The system extracts behavioral features and applies machine learning and deep learning models to distinguish legitimate users from impostors. A weighted sliding window mechanism enables continuous verification during an active session, allowing the system to detect abnormal behavior and trigger re-authentication when necessary. Experimental results demonstrate that the proposed approach achieves high authentication accuracy while improving security against session hijacking attacks

Index Keywords
Behavioral featuresSecond levelauthentication Neuralnetworks Deep learning

Author Affiliations

More RushikeshCorresponding Author · Department of Computer Science & Engineering, IIT Bombay, India
vedant aherResearch Partner · Department of Computer Science & Engineering, IIT Bombay, India

References Listing (3)

  1. [1]K. R. Rao and J. Doe, "High-resolution convolutional modeling in thermal imaging systems," IEEE Trans. Image Process., vol. 31, pp. 210–222, Jan. 2024.
  2. [2]W. Liu, R. Chen, and M. Rossi, "Deep residual neural networks for noise-reduction and super-resolution edge rebuilding," Pattern Recognition, vol. 142, pp. 110-125, Oct. 2025.
  3. [3]S. Jenkins, "Cooperative token-heuristics inside warehousing routing grids," Int. J. Rob. Res., vol. 18, no. 4, pp. 450–467, May 2025.