Athanasios Bamis

Designing challenge questions for location‐based authentication systems: a real‐life study

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.

Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data

Depression is a serious health disorder. In this study, we investigate the feasibility of depression screening using sensor data collected from smartphones. We extract various behavioral features from smartphone sensing data and investigate the efficacy of various machine learning tools to predict clinical diagnoses and PHQ-9 scores (a quantitative tool for aiding depression screening in practice). A notable feature of our study is that we leverage a dataset that includes clinical ground truth. We find that behavioral data from smartphones can predict clinical depression with good accuracy. In addition, combining behavioral data and PHQ-9 scores can provide prediction accuracy significantly exceeding each in isolation, indicating that behavioral data captures relevant features that are not reflected by PHQ-9 scores. Finally, we develop multi-feature regression models for PHQ-9 scores that achieve significantly improved accuracy compared to direct regression models based on single features.

Invoking Web Services Based on Energy Consumption Models

Web service consumption may account for a nonnegligible share of the energy that is consumed by mobile applications. Unawareness of the energy consumption characteristics of Web service-based applications during development may cause the battery of devices, e.g., smartphones, to run out more frequently. Compared to related experimental energy consumption studies, the work at hand is the first work that focuses on factors which are specific to services computing, such as the timing of Web service invocations and the Web service response caching logic. Further, Web service invocations are the only variable energy-consuming activity included in the experiments. Based on the results, it is shown, firstly, how the execution of exactly the same Web service invocations may lead to energy consumption results that present differences of up to ca. 15% for WLAN and ca. 60% for UMTS connections, and, secondly, how rules and techniques for energy-efficient development of mobile Web service-based applications can be extracted from the gained knowledge.

A Location-Based Authentication System Leveraging Smartphones

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 users own data. The second set is generated based on a randomly selected users data. Additionally, in cases of paired participants, each user is presented with a third set of questions which is generated based on the users friends 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.

Locating emergencies in a campus using wi-fi access point association data

Despite much progress in emergency management, effective techniques for real-time tracking of emergency events are still lacking. We envision a promising direction to achieve real-time emergency tracking is through widely adopted smartphones. In this paper, we explore the first step in achieving this goal, namely, locating emergency in real time using smartphones. Our main contribution is a novel approach that locates emergencies by analyzing AP (access point) association events collected from a campus Wi-Fi network. It is motivated by the observation that human behavior and mobility pattern are significantly altered in the face of emergency, which is reflected in how their smartphones associate with the APs in the network. More specifically, our approach locates emergency by discovering APs with abnormal association patterns using Extreme Value Theory (EVT). Preliminary evaluation using real data collected from a university campus network demonstrates the effectiveness of our approach.

Multi-view Bi-clustering to Identify Smartphone Sensing Features Indicative of Depression

Depression is a major public health issue with direct and significant effects on both physical and mental health. In this study, we analyze smartphone sensing data to find differential behavioral features that are correlated with depression measures such as patient health questionnaire (PHQ-9). Our approach uses an innovative multi-view bi-clustering algorithm. It takes multiple views of sensing data as input to identify homogeneous behavioral groups and simultaneously the key sensing features that characterize the different groups. Using a publicly available dataset, we discover that these behavioral groups with differential sensing features are highly discriminative of PHQ-9 scores that are self reported by the study subjects. For instance, the group comprising less active users in the sensed activities corresponds to overall higher PHQ-9 scores. We then employ the key sensing features that distinguish the different groups to create predictive models to predict the group assignment of individuals. We verify the generalizability of these models using the support vector machine classifier. Cross validation studies show that our classifiers can classify individuals into the correct subgroups with an overall accuracy of 87%.

Tracking appliance usage information in residential settings using off-the-shelf low-frequency meters

Given the ongoing widespread deployment of low frequency electricity sub-metering devices at residential and commercial buildings, fine-grained usage information of end-loads can bring a new powerful sensing modality in Cyber-Physical Systems (CPS). Motivated by the opportunity, this paper describes an algorithm of estimating the ON/OFF sequences for typical household end-loads in close-to-real-time using an off-the-shelf power meter. Unlike previous algorithms that lacks in scalability to support diverse applications in CPS our algorithm is designed to provide control knobs to support various trade-offs between accuracy and computation load or delay to satisfy the different application requirements. We experimentally verify the proposed algorithm using a collection of home appliances. Our experiment result shows that our algorithm is able to detect ON/OFF sequences of 7 appliances nearly without error and 3 appliances with moderate error rate less than 6% among 12 typical household appliances.

A method for improving mobile authentication using human spatio-temporal behavior

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 devices user based on her recent macroscopic behavior over space and time. The users 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 users recent behavior against the trained model. Our first evaluation results demonstrate the ability of the proposed approach to detect anomalies in the users behavior without generating a significant number of false alarms.

Towards macroscopic human behavior based authentication for mobile transactions

Integration of Near Field Communication (NFC) sensors into mobile devices has enabled their use for authentication. The ubiquitous nature of authentication using mobile devices comes though with increased security considerations. In addition to the risk of being stolen, mobile devices are increasingly susceptible to different types of software attacks. User-provided passwords such as Personal Identification Numbers (PINs) are often employed to ameliorate these limitations. The use of passwords though, has its own vulnerabilities that are mainly caused by the passwords static nature and low entropy. To eliminate the security threats caused by untrusted devices and static passwords, we propose the development of new types of biometric authentication, based on macroscopic human behavior.

Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning

Depression is a common mood disorder that causes severe medical problems and interferes negatively with daily life. Identifying human behavior patterns that are predictive or indicative of depressive disorder is important. Clinical diagnosis of depression relies on costly clinician assessment using survey instruments which may not objectively reflect the fluctuation of daily behavior. Self-administered surveys, such as the Quick Inventory of Depressive Symptomatology (QIDS) commonly used to monitor depression, may show disparities from clinical decision. Smartphones provide easy access to many behavioral parameters, and Fitbit wrist bands are becoming another important tool to assess variables such as heart rates and sleep efficiency that are complementary to smartphone sensors. However, data used to identify depression indicators have been limited to a single platform either iPhone, or Android, or Fitbit alone due to the variation in their methods of data collection. The present work represents a large-scale effort to collect and integrate data from mobile phones, wearable devices, and self reports in depression analysis by designing a new machine learning approach. This approach constructs sparse mappings from sensing variables collected by various tools to two separate targets: self-reported QIDS scores and clinical assessment of depression severity. We propose a so-called heterogeneous multi-task feature learning method that jointly builds inference models for related tasks but of different types including classification and regression tasks. The proposed method was evaluated using data collected from 103 college students and could predict the QIDS score with an R2 reaching 0.44 and depression severity with an F1-score as high as 0.77. By imposing appropriate regularizers, our approach identified strong depression indicators such as time staying at home and total time asleep.