USER AUTHENTICATION ACROSS DEVICES, MODALITIES AND REPRESENTATION: BEHAVIORAL BIOMETRIC METHODS

Biometrics eliminate the need for a person to remember and reproduce complex secretive information or carry additional hardware in order to authenticate oneself. Behavioral biometrics is a branch of biometrics that focuses on using a person’s behavior or way of doing a task as means of authentication. These tasks can be any common, day to day tasks like walking, sleeping, talking, typing and so on. As interactions with computers and other smart-devices like phones and tablets have become an essential part of modern life, a person’s style of interaction with them can be used as a powerful means of behavioral biometrics. In this dissertation, we present insights from the analysis of our proposed set of contextsensitive or word-specific keystroke features on desktop, tablet and phone. We show that the conventional features are not highly discriminatory on desktops and are only marginally better on hand-held devices for user identification. By using information of the context, our proposed word-specific features offer superior discrimination among users on all devices. Classifiers, built using our proposed features, perform user identification with high accuracies in range of 90% to 97%, average precision and recall values of 0.914 and 0.901 respectively. Analysis of the word-based impact factors reveal that four or five character words, words with about 50% vowels, and those that are ranked higher on the frequency lists might give better results for the extraction and use of the proposed features for user identification. We also examine a large umbrella of behavioral biometric data such as; keystroke latencies, gait and swipe data on desktop, phone and tablet for the assumption of an underlying normal distribution, which is common in many research works. Using suitable nonparametric normality tests (Lilliefors test and Shapiro-Wilk test) we show that a majority of the features from all activities and all devices, do not follow a normal distribution. In most cases less than 25% of the samples that were tested had p values \u3e 0.05. We discuss alternate solutions to address the non-normality in behavioral biometric data. Openly available datasets did not provide the wide range of modalities and activities required for our research. Therefore, we have collected and shared an open access, large benchmark dataset for behavioral biometrics on IEEEDataport. We describe the collection and analysis of our Syracuse University and Assured Information Security - Behavioral Biometrics Multi-device and multi -Activity data from Same users (SU-AIS BB-MAS) Dataset. Which is an open access dataset on IEEEdataport, with data from 117 subjects for typing (both fixed and free text), gait (walking, upstairs and downstairs) and touch on Desktop, Tablet and Phone. The dataset consists a total of about: 3.5 million keystroke events; 57.1 million data-points for accelerometer and gyroscope each; 1.7 million datapoints for swipes and is listed as one of the most popular datasets on the portal (through IEEE emails to all members on 05/13/2020 and 07/21/2020). We also show that keystroke dynamics (KD) on a desktop can be used to classify the type of activity, either benign or adversarial, that a text sample originates from. We show the inefficiencies of popular temporal features for this task. With our proposed set of 14 features we achieve high accuracies (93% to 97%) and low Type 1 and Type 2 errors (3% to 8%) in classifying text samples of different sizes. We also present exploratory research in (a) authenticating users through musical notes generated by mapping their keystroke latencies to music and (b) authenticating users through the relationship between their keystroke latencies on multiple devices

DEFT: A new distance-based feature set for keystroke dynamics

Keystroke dynamics is a behavioural biometric utilised for user identification and authentication. We propose a new set of features based on the distance between keys on the keyboard, a concept that has not been considered before in keystroke dynamics. We combine flight times, a popular metric, with the distance between keys on the keyboard and call them as Distance Enhanced Flight Time features (DEFT). This novel approach provides comprehensive insights into a person's typing behaviour, surpassing typing velocity alone. We build a DEFT model by combining DEFT features with other previously used keystroke dynamic features. The DEFT model is designed to be device-agnostic, allowing us to evaluate its effectiveness across three commonly used devices: desktop, mobile, and tablet. The DEFT model outperforms the existing state-of-the-art methods when we evaluate its effectiveness across two datasets. We obtain accuracy rates exceeding 99% and equal error rates below 10% on all three devices.Comment: 12 pages, 5 figures, 3 tables, conference pape

On the Inference of Soft Biometrics from Typing Patterns Collected in a Multi-device Environment

In this paper, we study the inference of gender, major/minor (computer science, non-computer science), typing style, age, and height from the typing patterns collected from 117 individuals in a multi-device environment. The inference of the first three identifiers was considered as classification tasks, while the rest as regression tasks. For classification tasks, we benchmark the performance of six classical machine learning (ML) and four deep learning (DL) classifiers. On the other hand, for regression tasks, we evaluated three ML and four DL-based regressors. The overall experiment consisted of two text-entry (free and fixed) and four device (Desktop, Tablet, Phone, and Combined) configurations. The best arrangements achieved accuracies of 96.15%, 93.02%, and 87.80% for typing style, gender, and major/minor, respectively, and mean absolute errors of 1.77 years and 2.65 inches for age and height, respectively. The results are promising considering the variety of application scenarios that we have listed in this work.Comment: The first two authors contributed equally. The code is available upon request. Please contact the last autho

Effective Identity Management on Mobile Devices Using Multi-Sensor Measurements

Due to the dramatic increase in popularity of mobile devices in the past decade, sensitive user information is stored and accessed on these devices every day. Securing sensitive data stored and accessed from mobile devices, makes user-identity management a problem of paramount importance. The tension between security and usability renders the task of user-identity verification on mobile devices challenging. Meanwhile, an appropriate identity management approach is missing since most existing technologies for user-identity verification are either one-shot user verification or only work in restricted controlled environments. To solve the aforementioned problems, we investigated and sought approaches from the sensor data generated by human-mobile interactions. The data are collected from the on-board sensors, including voice data from microphone, acceleration data from accelerometer, angular acceleration data from gyroscope, magnetic force data from magnetometer, and multi-touch gesture input data from touchscreen. We studied the feasibility of extracting biometric and behaviour features from the on-board sensor data and how to efficiently employ the features extracted to perform user-identity verification on the smartphone device. Based on the experimental results of the single-sensor modalities, we further investigated how to integrate them with hardware such as fingerprint and Trust Zone to practically fulfill a usable identity management system for both local application and remote services control. User studies and on-device testing sessions were held for privacy and usability evaluation.Computer Science, Department o

Non-Intrusive Continuous User Authentication for Mobile Devices

The modern mobile device has become an everyday tool for users and business. Technological advancements in the device itself and the networks that connect them have enabled a range of services and data access which have introduced a subsequent increased security risk. Given the latter, the security requirements need to be re-evaluated and authentication is a key countermeasure in this regard. However, it has traditionally been poorly served and would benefit from research to better understand how authentication can be provided to establish sufficient trust. This thesis investigates the security requirements of mobile devices through literature as well as acquiring the user’s perspectives. Given the findings it proposes biometric authentication as a means to establish a more trustworthy approach to user authentication and considers the applicability and topology considerations. Given the different risk and requirements, an authentication framework that offers transparent and continuous is developed. A thorough end-user evaluation of the model demonstrates many positive aspects of transparent authentication. The technical evaluation however, does raise a number of operational challenges that are difficult to achieve in a practical deployment. The research continues to model and simulate the operation of the framework in an controlled environment seeking to identify and correlate the key attributes of the system. Based upon these results and a number of novel adaptations are proposed to overcome the operational challenges and improve upon the impostor detection rate. The new approach to the framework simplifies the approach significantly and improves upon the security of the system, whilst maintaining an acceptable level of usability

Federated Authentication using the Cloud (Cloud Aura)

Individuals, businesses and governments undertake an ever-growing range of activities online and via various Internet-enabled digital devices. Unfortunately, these activities, services, information and devices are the targets of cybercrimes. Verifying the user legitimacy to use/access a digital device or service has become of the utmost importance. Authentication is the frontline countermeasure of ensuring only the authorised user is granted access; however, it has historically suffered from a range of issues related to the security and usability of the approaches. Traditionally deployed in a point-of-entry mode (although a number of implementations also provide for re-authentication), the intrusive nature of the control is a significant inhibitor. Thus, it is apparent that a more innovative, convenient and secure user authentication solution is vital. This thesis reviews the authentication methods along with the current use of authentication technologies, aiming at developing a current state-of-the-art and identifying the open problems to be tackled and available solutions to be adopted. It also investigates whether these authentication technologies have the capability to fill the gap between the need for high security whilst maximising user satisfaction. This is followed by a comprehensive literature survey and critical analysis of the existing research domain on continuous and transparent multibiometric authentication. It is evident that most of the undertaken studies and proposed solutions thus far endure one or more shortcomings; for instance, an inability to balance the trade-off between security and usability, confinement to specific devices, lack or negligence of evaluating users’ acceptance and privacy measures, and insufficiency or absence of real tested datasets. It concludes that providing users with adequate protection and convenience requires innovative robust authentication mechanisms to be utilised in a universal manner. Accordingly, it is paramount to have a high level of performance, scalability, and interoperability amongst existing and future systems, services and devices. A survey of 302 digital device users was undertaken and reveals that despite the widespread interest in more security, there is a quite low number of respondents using or maintaining the available security measures. However, it is apparent that users do not avoid applying the concept of authentication security but avoid the inconvenience of its current common techniques (biometrics are having growing practical interest). The respondents’ perceptions towards Trusted Third-Party (TTP) enable utilising biometrics for a novel authentication solution managed by a TTP working on multiple devices to access multiple services. However, it must be developed and implemented considerately. A series of experimental feasibility analysis studies disclose that even though prior Transparent Authentication Systems (TAS) models performed relatively well in practice on real live user data, an enhanced model utilising multibiometric fusion outweighs them in terms of the security and transparency of the system within a device. It is also empirically established that a centralised federated authentication approach using the Cloud would help towards constructing a better user profile encompassing multibiometrics and soft biometric information from their multiple devices and thus improving the security and convenience of the technique beyond those of unimodal, the Non-Intrusive and Continuous Authentication (NICA), and the Weighted Majority Voting Fusion (WMVF) and what a single device can do by itself. Furthermore, it reduces the intrusive authentication requests by 62%-74% (of the total assumed intrusive requests without operating this model) in the worst cases. As such, the thesis proposes a novel authentication architecture, which is capable of operating in a transparent, continuous and convenient manner whilst functioning across a range of digital devices – bearing in mind it is desirable to work on differing hardware configurations, operating systems, processing capabilities and network connectivity but they are yet to be validated. The approach, entitled Cloud Aura, can achieve high levels of transparency thereby being less dependent on secret-knowledge or any other intrusive login and leveraging the available devices capabilities without requiring any external sensors. Cloud Aura incorporates a variety of biometrics from different types, i.e. physiological, behavioural, and soft biometrics and deploys an on-going identity confidence level based upon them, which is subsequently reflected on the user privileges and mapped to the risk level associated to them, resulting in relevant reaction(s). While in use, it functions with minimal processing overhead thereby reducing the time required for the authentication decision. Ultimately, a functional proof of concept prototype is developed showing that Cloud Aura is feasible and would have the provisions of effective security and user convenience.Royal Commission for Jubail and Yanbu, Kingdom of Saudi Arabi

Image-based Authentication

Mobile and wearable devices are popular platforms for accessing online services. However, the small form factor of such devices, makes a secure and practical experience for user authentication, challenging. Further, online fraud that includes phishing attacks, has revealed the importance of conversely providing solutions for usable authentication of remote services to online users. In this thesis, we introduce image-based solutions for mutual authentication between a user and a remote service provider. First, we propose and develop Pixie, a two-factor, object-based authentication solution for camera-equipped mobile and wearable devices. We further design ai.lock, a system that reliably extracts from images, authentication credentials similar to biometrics. Second, we introduce CEAL, a system to generate visual key fingerprint representations of arbitrary binary strings, to be used to visually authenticate online entities and their cryptographic keys. CEAL leverages deep learning to capture the target style and domain of training images, into a generator model from a large collection of sample images rather than hand curated as a collection of rules, hence provides a unique capacity for easy customizability. CEAL integrates a model of the visual discriminative ability of human perception, hence the resulting fingerprint image generator avoids mapping distinct keys to images which are not distinguishable by humans. Further, CEAL deterministically generates visually pleasing fingerprint images from an input vector where the vector components are designated to represent visual properties which are either readily perceptible to human eye, or imperceptible yet are necessary for accurately modeling the target image domain. We show that image-based authentication using Pixie is usable and fast, while ai.lock extracts authentication credentials that exceed the entropy of biometrics. Further, we show that CEAL outperforms state-of-the-art solution in terms of efficiency, usability, and resilience to powerful adversarial attacks