A bio-signal based framework to secure mobile devices. (1st July 2017)

• Record Type: Journal Article
• Authors: Kumar, Pradeep; Saini, Rajkumar; Pratim Roy, Partha; Prosad Dogra, Debi
• Is Part Of: Journal of network and computer applications. Volume 89(2017)
• Page Start: 62
• Page End: 71
• Publication Date: 2017-07-01
• Subjects: Security of mobile devices -- EEG -- Authentication -- Security -- HMM -- SVM; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2017.02.011
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Nowadays, mobile devices are often equipped with high-end processing units and large storage space. Mobile users usually store personal, official, and large amount of multimedia data. Security of such devices are mainly dependent on PIN (personal identification number), password, bio-metric data, or gestures/patterns. However, these mechanisms have a lot of security vulnerabilities and prone to various types of attacks such as shoulder surfing. The uniqueness of Electroencephalography (EEG) signal can be exploited to remove some of the drawbacks of the existing systems. Such signals can be recorded and transmitted through wireless medium for processing. In this paper, we propose a new framework to secure mobile devices using EEG signals along with existing pattern-based authentication. The pattern based authentication passwords are considered as identification tokens. We have investigated the use of EEG signals recorded during pattern drawing over the screen of the mobile device in the authentication phase. To accomplish this, we have collected EEG signals of 50 users while drawing different patterns. The robustness of the system has been evaluated against 2400 unauthorized attempts made by 30 unauthorized users who have tried to gain access of the device using known patterns of 20 genuine users. EEG signals are modeled using Hidden Markov Model (HMM), and using a binary classifier implemented with Support Vector Machine (SVM) to verify the authenticity of a test pattern. Verification performances are measured using three popular security matrices, namely Detection Error Trade-off (DET), Half Total Error Rate (HTER), and Receiver Operating Characteristic (ROC) curves. Our experiments revel that, the method is promising and can be a possible alternative to develop robust authentication protocols for hand-held devices.