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 this section, some of the most recent published literature on active authentication and the utilization of application usage data are reviewed. The section also discusses the exploitation of two active authentication datasets (UMDAA-02 @cite_3 and Securacy @cite_10 ) for this research work.
@cite_10 @cite_3
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In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors — front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research. Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this paper, we collect and analyze behavioral biometrics data from 200 subjects, each using their personal Android mobile device for a period of at least 30 days. This data set is novel in the context of active authentication due to its size, duration, number of modalities, and absence of restrictions on tracked activity. The geographical colocation of the subjects in the study is representative of a large closed-world environment such as an organization where the unauthorized user of a device is likely to be an insider threat: coming from within the organization. We consider four biometric modalities: 1) text entered via soft keyboard, 2) applications used, 3) websites visited, and 4) physical location of the device as determined from GPS (when outdoors) or WiFi (when indoors). We implement and test a classifier for each modality and organize the classifiers as a parallel binary decision fusion architecture. We are able to characterize the performance of the system with respect to intruder detection time and to quantify the contribution of each modality to the overall performance. This paper presents the results of a security and usability review of the authentication implementations used by more than 10 UK banks. Our focus is on their use of dual text credentials that combine two passwords, PINs, or challenge questions (and some “partial selection” variations). We model the authentication protocols based upon several deployment choices, such as the credential rules, and use the model to compare the security and usability properties of the implementations. Our results indicate some variation and inconsistency across the UK banking industry, from which we offer some suggestions for improved authentication protocol design. This paper describes an experiment to investigate user perceptions of the usability and security of single-factor and two-factor authentication methods in automated telephone banking. In a controlled experiment with 62 banking customers a knowledge-based, single-factor authentication procedure, based on those commonly used in the financial services industry, was compared with a two-factor approach where in addition to the knowledge-based step, a one-time passcode was generated using a hardware security token. Results were gathered on the usability and perceived security of the two methods described, together with call completion rates and call durations for the two methods. Significant differences were found between the two methods, with the two-factor version being perceived as offering higher levels of security than the single-factor authentication version; however, this gain was offset by significantly lower perceptions of usability, and lower ratings for convenience and ease of use for the two-factor version. In addition, the two-factor authentication version took longer for participants to complete. This research provides valuable empirical evidence of the trade-off between security and usability in automated systems.