Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A Statistical Analysis

Most keystroke dynamics studies have been evaluated using a specific kind of dataset in which users type an imposed login and password. Moreover, these studies are optimistics since most of them use different acquisition protocols, private datasets, controlled environment, etc. In order to enhance the accuracy of keystroke dynamics' performance, the main contribution of this paper is twofold. First, we provide a new kind of dataset in which users have typed both an imposed and a chosen pairs of logins and passwords. In addition, the keystroke dynamics samples are collected in a web-based uncontrolled environment (OS, keyboards, browser, etc.). Such kind of dataset is important since it provides us more realistic results of keystroke dynamics' performance in comparison to the literature (controlled environment, etc.). Second, we present a statistical analysis of well known assertions such as the relationship between performance and password size, impact of fusion schemes on system overall performance, and others such as the relationship between performance and entropy. We put into obviousness in this paper some new results on keystroke dynamics in realistic conditions.Comment: The Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 2012), Piraeus : Greece (2012

A Comparison of Authentication Methods via Keystroke Dynamics

Authentication systems based on keystroke dynamics analyze the typical typing pattern of a user when interacting with an input device, such as the keyboard of a computer. In the literature, three major approaches on keystroke dynamics can be found: distance-based, statistical-based and machine learning-based approaches, which are often used to solve the problem. Nevertheless, in the literature there are several works which results are obtained from different comparison methodologies; this represents a great problem for future researchers who seek to improve or advance with prior works. Furthermore, by using proprietary databases, researchers do not provide a good overview of the overall performance of their methods, but rather an overview in a specific case: That represented by their database. In this investigation, we proposed to evaluate the performance of the most representative classifiers in two of the three most common approaches used in keystroke dynamics using the public Greyc dataset. The experimental results, reveal that machine-learning based approaches outperformed the distance-based techniques. Moreover, the Random Forest classifier, provided encouraging results

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

Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets

The rapid growth of electronic assessment in various fields has led to the emergence of issues such as user identity fraud and cheating. One potential solution to these problems is to use a complementary authentication method, such as a behavioral biometric characteristic that is unique to each individual. One promising approach is keystroke dynamics, which involves analyzing the typing patterns of users. In this research, the Deep Belief Nets (DBN) model is used to implement a dynamic keystroke technique for secure e-assessment. The proposed system extracts various features from the pressure-time measurements, digraphs (dwell time and flight time), trigraphs, and n-graphs, and uses these features to classify the user's identity by applying the DBN algorithm to a dataset collected from participants who typed free text using a standard QWERTY keyboard in a neutral state without inducing specific emotions. The DBN model is designed to detect cheating attempts and is tested on a dataset collected from the proposed e-assessment system using free text. The implementation of the DBN results in an error rate of 5% and an accuracy of 95%, indicating that the system is effective in identifying users' identities and cheating, providing a secure e-assessment approach

Combating shoulder-surfing: a hidden button gesture based scheme

This project describes an authentication technique that is shoulder-surfing resistant. Shoulder surfing is an attack in which an attacker can get access to private information by observing the user’s interaction with a terminal, or by using recording tools to record the user interaction and study the obtained data, with the objective of obtaining unauthorized access to a target user’s personal information. The technique described here relies on gestural analysis coupled with a secondary channel of authentication that uses button pressing. The thesis presents and evaluates multiple alternative algorithms for gesture analysis, and furthermore assesses the effectiveness of the technique.Universidade da Madeir

Neutrophil count prediction in childhood cancer patients receiving 6-mercaptopurine chemotherapy treatment

Acute Lymphoblastic Leukaemia (ALL) is a common form of blood cancer, usually affecting children under 15 years of age. Chemotherapy treatment for ALL is delivered in three phases viz. induction (to achieve initial remission), intensification (to kill the majority of abnormal cells), and finally, maintenance. The maintenance phase involves oral administration of the chemotherapy drug 6-Mercaptopurine (6-MP) in varying doses to destroy any remaining abnormal cells and prevent reoccurrence. A key side effect of the treatment is a reduction in neutrophil counts that can result in a condition known as neutropenia, i.e. reduced immune system. This carries a risk of secondary infection and has been linked to 60% of ALL fatalities. Current practice aims to control neutrophil counts by varying 6-MP dosages on a weekly basis based on blood counts. However, its success is varied. This thesis proposes a number of intelligent prediction methods to more accurately predicting neutrophil counts one week ahead using blood count data and corresponding 6-MP dosing regimens. Firstly, a well-known and robust neural network (Nonlinear Autoregressive Exogenous) is applied to blood count data to provide an initial assessment of the feasibility of such an approach. A comparative analysis of a series of more complex algorithms is then considered for more advanced, in-depth analysis viz. Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM). Both methods are shown to have a prediction accuracy of around 60% on the first sample period, with the MLP also having a prediction accuracy of more than 60% in the second sample period in seven out of ten blood data points (there was 10 timeseries blood data predictions). However, in comparison the accuracy of SVM is relatively low. Finally, an incremental learning-based approach is proposed to increase the accuracy of the system and provide a realistic framework for real-time implementation. The accuracy is shown to improve considerably as more data is added, and the predicted neutrophils data is shown to follow the trend of the actual neutrophil counts

Advancing the technology of sclera recognition

PhD ThesisEmerging biometric traits have been suggested recently to overcome some challenges and issues related to utilising traditional human biometric traits such as the face, iris, and fingerprint. In particu- lar, iris recognition has achieved high accuracy rates under Near- InfraRed (NIR) spectrum and it is employed in many applications for security and identification purposes. However, as modern imaging devices operate in the visible spectrum capturing colour images, iris recognition has faced challenges when applied to coloured images especially with eye images which have a dark pigmentation. Other issues with iris recognition under NIR spectrum are the constraints on the capturing process resulting in failure-to-enrol, and degradation in system accuracy and performance. As a result, the research commu- nity investigated using other traits to support the iris biometric in the visible spectrum such as the sclera. The sclera which is commonly known as the white part of the eye includes a complex network of blood vessels and veins surrounding the eye. The vascular pattern within the sclera has different formations and layers providing powerful features for human identification. In addition, these blood vessels can be acquired in the visible spectrum and thus can be applied using ubiquitous camera-based devices. As a consequence, recent research has focused on developing sclera recog- nition. However, sclera recognition as any biometric system has issues and challenges which need to be addressed. These issues are mainly related to sclera segmentation, blood vessel enhancement, feature ex- traction, template registration, matching and decision methods. In addition, employing the sclera biometric in the wild where relaxed imaging constraints are utilised has introduced more challenges such as illumination variation, specular reflections, non-cooperative user capturing, sclera blocked region due to glasses and eyelashes, variation in capturing distance, multiple gaze directions, and eye rotation. The aim of this thesis is to address such sclera biometric challenges and highlight the potential of this trait. This also might inspire further research on tackling sclera recognition system issues. To overcome the vii above-mentioned issues and challenges, three major contributions are made which can be summarised as 1) designing an efficient sclera recognition system under constrained imaging conditions which in- clude new sclera segmentation, blood vessel enhancement, vascular binary network mapping and feature extraction, and template registra- tion techniques; 2) introducing a novel sclera recognition system under relaxed imaging constraints which exploits novel sclera segmentation, sclera template rotation alignment and distance scaling methods, and complex sclera features; 3) presenting solutions to tackle issues related to applying sclera recognition in a real-time application such as eye localisation, eye corner and gaze detection, together with a novel image quality metric. The evaluation of the proposed contributions is achieved using five databases having different properties representing various challenges and issues. These databases are the UBIRIS.v1, UBIRIS.v2, UTIRIS, MICHE, and an in-house database. The results in terms of segmen- tation accuracy, Equal Error Rate (EER), and processing time show significant improvement in the proposed systems compared to state- of-the-art methods.Ministry of Higher Education and Scientific Research in Iraq and the Iraqi Cultural Attach´e in Londo

Identifying users using Keystroke Dynamics and contextual information

Biometric identification systems based on Keystroke Dynamics have been around for almost forty years now. There has always been a lot of interest in identifying individuals using their physiological or behavioral traits. Keystroke Dynamics focuses on the particular way a person types on a keyboard. The objective of the proposed research is to determine how well the identity of users can be established when using this biometric trait and when contextual information is also taken into account. The proposed research focuses on free text. Users were never told what to type, how or when. This particular field of Keystroke Dynamics has not been as thoroughly studied as the fixed text alternative where a plethora of methods have been tried. The proposed methods focus on the hypothesis that the position of a particular letter, or combination of letters, in a word is of high importance. Other studies have not taken into account if these letter combinations had occurred at the beginning, the middle, or the end of a word. A template of the user will be built using the context of the written words and the latency between successive keystrokes. Other features, like word length, minimum number of needed words to consider a session valid, frequency of words, model building parameters, as well as age group and gender have also been studied to determine those that better help ascertain the identity of an individual. The results of the proposed research should help determine if using Keystroke Dynamics and the proposed methodology are enough to identify users from the content they type with a good enough level of certainty. From this moment, it could be used as a method to ensure that a user is not supplanted, in authentication schemes, or even to help determine the authorship of different parts of a document written by more than one user.Els sistemes d’identificació biomètrica basades en la cadència de tecleig fa gairebé quaranta anys que s’estudien. Hi ha hagut molt interès en identificar les persones a partir de les seves característiques fisiològiques o de comportament. La cadència de tecleig és la manera en la que una persona escriu en un teclat. L’objectiu de la recerca proposada és determinar com de bé es pot arribar a identificar un individu mitjançant aquesta característica biomètrica i quan també es prenen en consideració dades contextuals. Aquesta recerca es basa en text lliure. Als usuaris mai se’ls va dir què, quan o com havien d’escriure. Aquest camp de la cadència de tecleig no ha estat tan estudiat com l’alternativa de text fix on un gran ventall de mètodes s’han provat. Els mètodes d’identificació proposats es basen en la hipòtesi que la posició d’una lletra, o combinació de lletres teclejades, en una paraula és de gran importància. Altres estudis no prenen en consideració aquesta informació, és a dir, si la combinació de lletres s’ha produït al principi, al mig o al final de la paraula. Es crearà una empremta de l’usuari tenint en compte el context de les lletres en les paraules escrites i les latències entre pulsacions successives. Altres característiques com la mida de les paraules, el nombre mínim de paraules necessari per considerar una sessió vàlida, la freqüència de mots, els paràmetres de construcció dels models, així com el grup d’edat i el gènere també s’han estudiat per determinar quines són les que millor ajuden a identificar un individu. Els resultats de la recerca proposada haurien de permetre determinar si l’ús de la cadència de tecleig i els mètodes proposats són suficients per identificar els usuaris a partir del contingut que generen, sempre amb un cert marge d’error. En cas afirmatiu es podria introduir la tècnica proposada com un mètode més per assegurar que un usuari no és suplantat, en sistemes d’autenticació, o fins i tot per ajudar a determinar l’autoria de diferents parts d’un document que ha estat escrit per més d’un usuari