TY  - EJOU
AU  - Aseres, Animaw Kerie 
AU  - Beyene, Asrat Mulatu 
AU  - Tegegne, Lemlem Kassa 

TI  - Mobile Touch Dynamics–Based User Classification Using Machine Learning and Fusion Techniques
T2  - Journal of Cyber Security

PY  - 2026
VL  - 8
IS  - 1
SN  - 2579-0064

AB  - Conventional multi-factor and one-time authentication approaches, such as passwords and one-time passwords (OTPs), have become increasingly vulnerable to advanced attack methods, motivating the need for continuous authentication (CA) systems that can verify user identity throughout an active session rather than only at login. For such a system to be effective, it must analyze user behavior reliably and in real time. This paper presents a novel approach to implementing CA on mobile devices using tap and swipe behavioral biometrics combined with machine learning (ML) and multimodal fusion. The dataset was collected from 400 volunteer participants using BahriApp, a custom-developed Android application (Bahri meaning “behavior” in Amharic). Several models were then trained to classify user identity, including Logistic Regression, SVM, Random Forest, and deep learning models (1D-CNN and LSTM), evaluated with feature-level and decision-level fusion of the tap and swipe datasets. Feature-level fusion using LSTM performed best, achieving 97.1% accuracy and a 4.4% Equal Error Rate (EER), balancing usability and security; weighted decision-level fusion achieved 96.5% accuracy and a 4.8% EER. The main contributions of this paper are: a custom behavioral biometrics dataset constructed from African participants; comparative benchmarks across machine learning models and fusion strategies; and insights that inform the design of an adaptive continuous authentication scheme. The study’s main limitation is dataset size and session coverage: of the 400 participants, only 29.75% and 20.75% provided 15 or more sessions in the tap and swipe datasets, respectively. Future work will focus on larger and more diverse multimodal datasets.
KW  - Continuous authentication; behavioral biometrics; gait; fusion techniques

DO  - 10.32604/jcs.2026.086559