Forensic-by-Design Framework Using Multimodal Behavioral Biometrics for Mobile Pharmacy Security
Year: 2026
Type: article
Abstract: In the mobile pharmacy sector, digital transformation increases the exposure of sensitive data and opens up insider threats that are not easily addressed by traditional security mechanisms such as login authentication and encryption, whose traces are only collected after an incident and often do not meet evidentiary standards. In this study we develop a multimodal behavioral biometric framework based on Forensic-by-Design principles to passively and continuously detect user interaction anomalies in mobile pharmacy applications. We develop three modules with hybrid architecture (Long Short-Term Memory network for keystroke dynamics, eXtreme Gradient Boosting for scrolling behavior and time threshold heuristic for detecting input that comes from paste or auto-injection). The keystroke module was tested on the Carnegie Mellon University keystroke benchmark (51 subjects) session disjoint split over 5 training runs and the framework was piloted in a mobile pharmacy application with 12 users and 19 sessions. For the keystroke module using key release intervals equal error rate of 11.1% was obtained (identification accuracy 74.4%) when key hold times and press latencies were added. For copy-paste heuristic requiring three consecutive intervals below 50 ms false positives on 20,400 genuine typing samples dropped from 16.3% to 0%. These results show that the proposed multimodal approach can support forensic readiness of mobile pharmacy applications by generating forensic-relevant interaction traces but more testing on a larger user population is needed to validate the scroll module, fusion layer and external validity of the findings.
Source: SinkrOn
Institutions: Universitas Muhammadiyah Malang, Lambung Mangkurat University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $32
OpenAlex ID: W7223823397