Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Keystroke Dynamics Authentication For Collaborative Systems

We present in this paper a study on the ability and the benefits of using a keystroke dynamics authentication method for collaborative systems. Authentication is a challenging issue in order to guarantee the security of use of collaborative systems during the access control step. Many solutions exist in the state of the art such as the use of one time passwords or smart-cards. We focus in this paper on biometric based solutions that do not necessitate any additional sensor. Keystroke dynamics is an interesting solution as it uses only the keyboard and is invisible for users. Many methods have been published in this field. We make a comparative study of many of them considering the operational constraints of use for collaborative systems

Vulnerability analysis of cyber-behavioral biometric authentication

Research on cyber-behavioral biometric authentication has traditionally assumed naïve (or zero-effort) impostors who make no attempt to generate sophisticated forgeries of biometric samples. Given the plethora of adversarial technologies on the Internet, it is questionable as to whether the zero-effort threat model provides a realistic estimate of how these authentication systems would perform in the wake of adversity. To better evaluate the efficiency of these authentication systems, there is need for research on algorithmic attacks which simulate the state-of-the-art threats. To tackle this problem, we took the case of keystroke and touch-based authentication and developed a new family of algorithmic attacks which leverage the intrinsic instability and variability exhibited by users\u27 behavioral biometric patterns. For both fixed-text (or password-based) keystroke and continuous touch-based authentication, we: 1) Used a wide range of pattern analysis and statistical techniques to examine large repositories of biometrics data for weaknesses that could be exploited by adversaries to break these systems, 2) Designed algorithmic attacks whose mechanisms hinge around the discovered weaknesses, and 3) Rigorously analyzed the impact of the attacks on the best verification algorithms in the respective research domains. When launched against three high performance password-based keystroke verification systems, our attacks increased the mean Equal Error Rates (EERs) of the systems by between 28.6% and 84.4% relative to the traditional zero-effort attack. For the touch-based authentication system, the attacks performed even better, as they increased the system\u27s mean EER by between 338.8% and 1535.6% depending on parameters such as the failure-to-enroll threshold and the type of touch gesture subjected to attack. For both keystroke and touch-based authentication, we found that there was a small proportion of users who saw considerably greater performance degradation than others as a result of the attack. There was also a sub-set of users who were completely immune to the attacks. Our work exposes a previously unexplored weakness of keystroke and touch-based authentication and opens the door to the design of behavioral biometric systems which are resistant to statistical attacks

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

Poisoning Attacks on Learning-Based Keystroke Authentication and a Residue Feature Based Defense

Behavioral biometrics, such as keystroke dynamics, are characterized by relatively large variation in the input samples as compared to physiological biometrics such as fingerprints and iris. Recent advances in machine learning have resulted in behaviorbased pattern learning methods that obviate the effects of variation by mapping the variable behavior patterns to a unique identity with high accuracy. However, it has also exposed the learning systems to attacks that use updating mechanisms in learning by injecting imposter samples to deliberately drift the data to impostors’ patterns. Using the principles of adversarial drift, we develop a class of poisoning attacks, named Frog-Boiling attacks. The update samples are crafted with slow changes and random perturbations so that they can bypass the classifiers detection. Taking the case of keystroke dynamics which includes motoric and neurological learning, we demonstrate the success of our attack mechanism. We also present a detection mechanism for the frog-boiling attack that uses correlation between successive training samples to detect spurious input patterns. To measure the effect of adversarial drift in frog-boiling attack and the effectiveness of the proposed defense mechanism, we use traditional error rates such as FAR, FRR, and EER and the metric in terms of shifts in biometric menagerie

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

Non-Intrusive Subscriber Authentication for Next Generation Mobile Communication Systems

Merged with duplicate record 10026.1/753 on 14.03.2017 by CS (TIS)The last decade has witnessed massive growth in both the technological development, and the consumer adoption of mobile devices such as mobile handsets and PDAs. The recent introduction of wideband mobile networks has enabled the deployment of new services with access to traditionally well protected personal data, such as banking details or medical records. Secure user access to this data has however remained a function of the mobile device's authentication system, which is only protected from masquerade abuse by the traditional PIN, originally designed to protect against telephony abuse. This thesis presents novel research in relation to advanced subscriber authentication for mobile devices. The research began by assessing the threat of masquerade attacks on such devices by way of a survey of end users. This revealed that the current methods of mobile authentication remain extensively unused, leaving terminals highly vulnerable to masquerade attack. Further investigation revealed that, in the context of the more advanced wideband enabled services, users are receptive to many advanced authentication techniques and principles, including the discipline of biometrics which naturally lends itself to the area of advanced subscriber based authentication. To address the requirement for a more personal authentication capable of being applied in a continuous context, a novel non-intrusive biometric authentication technique was conceived, drawn from the discrete disciplines of biometrics and Auditory Evoked Responses. The technique forms a hybrid multi-modal biometric where variations in the behavioural stimulus of the human voice (due to the propagation effects of acoustic waves within the human head), are used to verify the identity o f a user. The resulting approach is known as the Head Authentication Technique (HAT). Evaluation of the HAT authentication process is realised in two stages. Firstly, the generic authentication procedures of registration and verification are automated within a prototype implementation. Secondly, a HAT demonstrator is used to evaluate the authentication process through a series of experimental trials involving a representative user community. The results from the trials confirm that multiple HAT samples from the same user exhibit a high degree of correlation, yet samples between users exhibit a high degree of discrepancy. Statistical analysis of the prototypes performance realised early system error rates of; FNMR = 6% and FMR = 0.025%. The results clearly demonstrate the authentication capabilities of this novel biometric approach and the contribution this new work can make to the protection of subscriber data in next generation mobile networks.Orange Personal Communication Services Lt