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.
In recent years, there has been a lot of focus on predicting individual and community-wise application usage patterns @cite_6 . For example, in @cite_33 , the authors investigate the ratio of local and global applications in the top usage list, the traffic pattern for different application categories, likelihood of co-occurrence of two different applications and such other patterns in usage. In this work, the authors identify traffic from distinct applications using HTTP signatures. On the other hand, in @cite_12 the authors use mobile in-app advertisements to identify the applications in network traces. Using the ad flow data, the authors tried to analyze the usage behavior of different types of applications. In @cite_2 , the authors analyzed the application-usage logs of over @math smartphone users worldwide to develop an app-usage prediction model that leverages user preferences, historical usage patterns, activities and shared aggregate patterns of application behavior.
@cite_12 @cite_33 @cite_6 @cite_2
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Recent years have seen an explosive growth in the number of mobile devices such as smart phones and tablets. This has resulted in a growing need of the operators to understand the usage patterns of the mobile apps used on these devices. Previous studies in this area have relied on volunteers using instrumented devices or using fields in the HTTP traffic such as User-Agent to identify the apps in network traces. However, the results of the former approach are difficult to be extrapolated to real-world scenario while the latter approach is not applicable to platforms like Android where developers generally use generic strings, that can not be used to identify the apps, in the User-Agent field. In this paper, we present a novel way of identifying Android apps in network traces using mobile in-app advertisements. Our preliminary experiments with real world traces show that this technique is promising for large scale mobile app usage pattern studies. We also present an analysis of the official Android market place from an advertising perspective. Smartphone users are increasingly shifting to using apps as "gateways" to Internet services rather than traditional web browsers. App marketplaces for iOS, Android, and Windows Phone platforms have made it attractive for developers to deploy apps and easy for users to discover and start using many network-enabled apps quickly. For example, it was recently reported that the iOS AppStore has more than 350K apps and more than 10 billion downloads. Furthermore, the appearance of tablets and mobile devices with other form factors, which also use these marketplaces, has increased the diversity in apps and their user population. Despite the increasing importance of apps as gateways to network services, we have a much sparser understanding of how, where, and when they are used compared to traditional web services, particularly at scale. This paper takes a first step in addressing this knowledge gap by presenting results on app usage at a national level using anonymized network measurements from a tier-1 cellular carrier in the U.S. We identify traffic from distinct marketplace apps based on HTTP signatures and present aggregate results on their spatial and temporal prevalence, locality, and correlation. Abstract Smartphones nowadays have become indispensable personal gadgets to support our activities in almost every aspect of our lives. Thanks to the tremendous advancement of smartphone technologies, platforms, as well as the enthusiasm of individual developers, numerous mobile applications (apps) have been created to serve a wide range of usage purposes, making our daily life more convenient. While these apps are used, data logs are typically generated and ambience context is recorded forming a rich data source of the smartphone users’ behaviors. In this paper, we survey existing studies on mining smartphone data for uncovering app usage patterns leveraging such a data source. Our discussions of the studies are organized according to two main research streams, namely app usage prediction and app recommendations alongside a few other related studies. Finally, we also present several challenges and opportunities in the emerging area of mining smartphone usage patterns. Reliable smartphone app prediction can strongly benefit both users and phone system performance alike. However, real-world smartphone app usage behavior is a complex phenomena driven by a number of competing factors. In this pa- per, we develop an app usage prediction model that leverages three key everyday factors that affect app usage decisions -- (1) intrinsic user app preferences and user historical patterns; (2) user activities and the environment as observed through sensor-based contextual signals; and, (3) the shared aggregate patterns of app behavior that appear in various user communities. While rapid progress has been made recently in smartphone app prediction, existing prediction models tend to focus on only one of these factors. We evaluate a multi-faceted approach to prediction using (1) a 3-week 35-user field trial, along with (2) analysis of app usage logs of 4,606 smartphone users worldwide. We find our app usage model can not only produce more robust app predictions than conventional techniques, but it can also enable significant smartphone system optimizations.