BACKGROUND:With millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition. However, selection of EEG features used to answer experimental questions is typically determined a priori. The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically.METHODS:Schizophrenia (SZ; n = 40) and healthy community (HC; n = 12) subjects completed a Sternberg Working Memory Task (SWMT) during EEG recording. EEG was analyzed to extract 5 frequency components (theta1, theta2, alpha, beta, gamma) at 4 processing stages (baseline, encoding, retention, retrieval) and 3 scalp sites (frontal-Fz, central-Cz, occipital-Oz) separately for correctly and incorrectly answered trials. The 1-norm support vector machine (SVM) method was used to build EEG classifiers of SWMT trial accuracy (correct vs. incorrect; Model 1) and diagnosis (HC vs. SZ; Model 2). External validity of SVM models was examined in relation to neuropsychological test performance and diagnostic classification using conventional regression-based analyses.RESULTS:SWMT performance was significantly reduced in SZ (p < .001). Model 1 correctly classified trial accuracy at 84 % in HC, and at 74 % when cross-validated in SZ data. Frontal gamma at encoding and central theta at retention provided highest weightings, accounting for 76 % of variance in SWMT scores and 42 % variance in neuropsychological test performance across samples. Model 2 identified frontal theta at baseline and frontal alpha during retrieval as primary classifiers of diagnosis, providing 87 % classification accuracy as a discriminant function.CONCLUSIONS:EEG features derived by SVM are consistent with literature reports of gamma's role in memory encoding, engagement of theta during memory retention, and elevated resting low-frequency activity in schizophrenia. Tests of model performance and cross-validation support the stability and generalizability of results, and utility of SVM as an analytic approach for EEG feature selection.

Online service providers often use challenge questions (a.k.a. knowledge‐based authentication) to facilitate resetting of passwords or to provide an extra layer of security for authentication. While prior schemes explored both static and dynamic challenge questions to improve security, they do not systematically investigate the problem of designing challenge questions and its effect on user recall performance. Interestingly, as answering different styles of questions may require different amount of cognitive effort and evoke different reactions among users, we argue that the style of challenge questions itself can have a significant effect on user recall performance and usability of such systems. To address this void and investigate the effect of question types on user performance, this paper explores location‐based challenge question generation schemes where different types of questions are generated based on users’ locations tracked by smartphones and presented to users. For evaluation, we deployed our location tracking application on users’ smartphones and conducted two real‐life studies using four different kinds of challenge questions. Each study was approximately 30 days long and had 14 and 15 users respectively. Our findings suggest that the question type can have a significant effect on user performance. Finally, as individual users may vary in terms of performance and recall rate, we investigate and present a Bayesian classifier based authentication algorithm that can authenticate legitimate users with high accuracy by leveraging individual response patterns while reducing the success rate of adversaries.

This paper investigates a location-based authentication system where authentication questions are generated based on users' locations tracked by smartphones. More specifically, the system builds a location profile for a user based on periodically logged Wi-Fi access point beacons over time, and leverages this location profile to generate authentication questions. To evaluate the various aspects of this location-based authentication approach, we deployed the application on users' smartphones and conducted a real-life study for one month with 14 users. To simulate various kinds of adversaries (e.g., Naive vs. Knowledgeable), in our study, we recruited volunteers in pairs (e.g., Friends), in addition to single participants. Over the course of the experiment, each user is periodically presented with two sets of authentication questions. The first set is generated based on a user's own data. The second set is generated based on a randomly selected user's data. Additionally, in cases of paired participants, each user is presented with a third set of questions which is generated based on the user's friend's data. In each case, three different kinds of questions of varying difficulty levels are generated and presented to the user. Finally, we present a Bayesian classifier based authentication algorithm that can authenticate legitimate users with high accuracy by leveraging individual response patterns. We also discuss various aspects of location-based authentication mechanisms based on our findings in this paper.

Integration of NFC radios into smartphones is expediting the adoption of mobile devices as the preferred method for accessing physical locations, bank accounts, and other valuable resources. The pervasive nature of authentication using these mobile devices, however, comes with increased security considerations stemming from the possibility of physical loss of the device. To minimize the risk caused by stolen devices, this paper introduces a method for confirming the identity of a device's user based on her recent macroscopic behavior over space and time. The user's behavior is continuously recorded by a set of devices embedded in the environment (e.g., Wi-Fi Access Point) and used to train a probabilistic n-gram model. Subsequently, deviations caused by stolen devices can be detected by comparing the user's recent behavior against the trained model. Our first evaluation results demonstrate the ability of the proposed approach to detect anomalies in the user's behavior without generating a significant number of false alarms.