Using Keystroke Analysis as a mechanism for Subscriber Authentication on Mobile Handsets |
The mobile communications industry will experience an evolutionary step within the next two years with the introduction of third generation mobile networks, completing the handset transition from a purely telephony device of the first generation analogue networks into a multimedia multi-purpose mobile communications tool. The ability of these new handsets to store and access sensitive information such as financial records, digital certificates and company records in association with a large handset penetration (864 million subscribers) makes them a desirable target for impostors. The authentication technique for current mobile phones has many weaknesses from a technological and subscriber perspective, and as such non-intrusive and stronger subscriber authentication techniques are required. This study investigates the plausibility of one such technique that of keystroke analysis, comparing and contrasting a number of pattern recognition and neural network based approaches to classification. It was found that neural network-based approaches performed substantially better than the pattern recognition-based approaches with false acceptance and false rejection rates of 3.2%.
Clarke NL, Furnell SM, Lines BL, Reynolds PL