An empirical investigation of active continuous authentication for smartphones is presented in this paper by exploiting users' unique application usage data, i.e., distinct patterns of use, modeled by a Markovian process. Variations of Hidden Markov Models (HMMs) are evaluated for continuous user verification, and challenges due to the sparsity of session-wise data, an explosion of states, and handling unforeseen events in the test data are tackled. Unlike traditional approaches, the proposed formulation does not depend on the top N-apps, rather uses the complete app-usage information to achieve low latency. Through experimentation, empirical assessment of the impact of unforeseen events, i.e., unknown applications and unforeseen observations, on user verification is done via a modified edit-distance algorithm for simple sequence matching. It is found that for enhanced verification performance, unforeseen events should be incorporated in the models by adopting smoothing techniques with HMMs. For validation, extensive experiments on two distinct datasets are performed. The marginal smoothing technique is the most effective for user verification in terms of equal error rate (EER) and with a sampling rate of 1 30s^ -1 and 30 minutes of historical data, and the method is capable of detecting an intrusion within 2.5 minutes of application use.
An interesting use-case of application-usage data is presented in @cite_37 . The authors used a large-scale annotated application-usage dataset to build a predictor that can estimate where a person is (, at home or office) and if he she is with a close friend or a family member. In @cite_27 , the authors used application usage traces along with system status and sensor indicators to predict the battery life of the phones using machine learning techniques.
@cite_27 @cite_37
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Smartphones and smartphone apps have undergone an explosive growth in the past decade. However, smartphone battery technology hasn't been able to keep pace with the rapid growth of the capacity and the functionality of smartphones and apps. As a result, battery has always been a bottleneck of a user's daily experience of smartphones. An accurate estimation of the remaining battery life could tremendously help the user to schedule their activities and use their smartphones more efficiently. Existing studies on battery life prediction have been primitive due to the lack of real-world smartphone usage data at scale. This paper presents a novel method that uses the state-of-the-art machine learning models for battery life prediction, based on comprehensive and real-time usage traces collected from smartphones. The proposed method is the first that identifies and addresses the severe data missing problem in this context, using a principled statistical metric called the concordance index. The method is evaluated using a dataset collected from 51 users for 21 months, which covers comprehensive and fine-grained smartphone usage traces including system status, sensor indicators, system events, and app status. We find that the remaining battery life of a smartphone can be accurately predicted based on how the user uses the device at the real-time, in the current session, and in history. The machine learning models successfully identify predictive features for battery life and their applicable scenarios. Personal interactions using cell phones are so embedded in our everyday lives that they go almost unnoticed. We may try to protect ourselves from releasing sensitive information by increasing privacy protections, but how much can be inferred from our most basic phone usage? Using a large-scale annotated dataset of cell phone usage, we build a predictor to determine location context (home, work, commute) and social relationships (with close friend, with family) based on the clock of the phone and sequences of apps executed. Surprisingly, we show that just using this basic information we can accurately predict whether someone is at home, at work, and or with close friends, family. We note that this is almost inevitable because it only depends on using the phone and not the privacy settings. Our results suggest that our relationship with technology gives away more than we might suspect. This presents opportunities and challenges discussed in this paper.