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As modern mobile devices increase in their capability and accessibility, they introduce additional demands in terms of security - particularly authentication. With the widely documented poor use of PINs, Active Authentication is designed to overcome the fundamental issue of usable and secure authentication through utilizing biometric-based techniques to continuously verify user identity. This paper proposes a novel text-based multimodal biometric approach utilizing linguistic analysis, keystroke dynamics and behavioural profiling. Experimental investigations show that users can be discriminated via their text-based entry, with an average Equal Error Rate (EER) of 3.3%. Based on these findings, a framework that is able to provide robust, continuous and transparent authentication is proposed. The framework is evaluated to examine the effectiveness of providing security and user convenience. The result showed that the framework is able to provide a 91% reduction in the number of intrusive authentication requests required for high security applications

Abstract. The potential advantages of behavioural biometrics are that they can be utilised in a transparent (non-intrusive) and continuous authentication system. However, individual biometric techniques are not suited to all users and scenarios. One way to increase the reliability of transparent and continuous authentication systems is create a multi-modal behavioural biometric authentication system. This research investigated three behavioural biometric techniques based on SMS texting activities and messages, looking to apply these techniques as a multi-modal biometric authentication method for mobile devices. The results showed that behaviour profiling, keystroke dynamics and linguistic profiling can be used to discriminate users with overall error rates 20%, 20% and 22% respectively. To study the feasibility of multi-modal behaviour biometric authentication system, matching-level fusion methods were applied. Two fusion methods were utilised: simple sum and weight average. The results showed clearly that matching-level fusion can improve the classification performance with an overall EER 8%.

Mobile devices have become a ubiquitous technology that are also inherently intertwined with modern society. They have enabled a revolution of how people engage and interact with technology, computing and the Internet. However, as their popularity has increased, so have the threats against them. The paper presents the findings of a survey undertaken to examine users' attitudes and opinions towards security for their mobile device. The results are based upon a respondent population of 301 and show a pattern of users being concerned about security for their device (68%), wanting additional security (63%), yet not engaging with the security they are provided with. Only 54% of respondents utilise a PIN for authentication against a backdrop of 46% of respondents experiencing some form of security breach. Interestingly, the results do show a preference for security to be preinstalled and activated out-of-the-box (84%), placing a responsibility on network operators and/or manufacturers to provide sufficient controls.