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Currently, people uses mobile devices as a part of their lives. This leads to the risk of data leaking or stealing. Due to the security issue, there are many studies try to increase security in these devices. The authentication process was bringing to the device with hope that it will prevent users from the security's risk. Since the technology is developing fast, a new kind of risk comes to users without doubt. This study proposed a new study of feasibility to use keystroke dynamics on touchscreen devices. Using combination of four factors from keystroke dynamics, which are dwell time, interval time, interval timing ratio and distance between buttons, this study gives an accuracy at 82.18% with FAR and FRR at 0.020 and 0.178 respectively.

We consider the problem of learning nondeterministic finite state machines (NFSMs) from systems where their internal structures are implicit and nondeterministic. Recently, an algorithm for inferring observable NFSMs (ONFSMs), which are the potentially learnable subclass of NFSMs, has been proposed based on the hypothesis that the complete testing assumption is satisfied. According to this assumption, with an input sequence (query), the complete set of all possible output sequences is given by the so-called Teacher, so the number of times for asking the same query is not taken into account in the algorithm. In this paper, we proposeLNM*, a refined ONFSM learning algorithm that considers the amount for repeating the same query as one parameter. Unlike the previous work, our approach does not require all possible output sequences in one answer. Instead, it tries to observe the possible output sequences by asking the same query many times to the Teacher. We have proved thatLNM*can infer the corresponding ONFSMs of the unknown systems when the number of tries for the same query is adequate to guarantee the complete testing assumption. Moreover, the proof shows that our algorithm will eventually terminate no matter whether the assumption is fulfilled or not. We also present the theoretical time complexity analysis ofLNM*. In addition, experimental results demonstrate the practical efficiency of our approach.

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