Unimodal Touch Behaviour-Based User Authentication Using Deep Learning and Swarm Intelligence for Smartphones

Smartphones have become necessary in everyday life since they make communication, financial transactions, and data access easier. However, their broad use poses serious security risks, especially regarding ongoing user authentication. Traditional authentication techniques, including PINs, passwords, and patterns, only authenticate users at points of entry, leaving devices open to replay attacks, session hijacking, and spoofing. To overcome these constraints, the hybrid authentication approach proposed in this research uses multimodal touch behaviour for real-time identity verification. Using the Touchalytics dataset, this method combines motion sensor data from accelerometers, gyroscopes, and magnetometers with fine-grained touch attributes, including touch area, pressure, finger orientation, and typing dynamics. Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are combined in the system’s deep learning (DL) architecture for sequential touch analysis, and optimization approaches are used to improve model performance. The model captures detailed touch behaviour and motion sensor data, with hyperparameter tuning applied using Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CSO), and Sea-Horse Optimization (SHO). The CNN-LSTM + PSO model outperforms standalone DL models by achieving 99.86% accuracy with a False Acceptance Rate (FAR) of 0.0009, False Rejection Rate (FRR) of 0.0012, and Equal Error Rate (EER) of 0.001, according to extensive assessment on the Touchalytics dataset. For next-generation mobile security, this combination of Swarm Intelligence (SI) and DL provides a strong, flexible, and effective authentication architecture.