Fawaz A. Alsulaiman

Three-Dimensional Password for More Secure Authentication

Current authentication systems suffer from many weaknesses. Textual passwords are commonly used; however, users do not follow their requirements. Users tend to choose meaningful words from dictionaries, which make textual passwords easy to break and vulnerable to dictionary or brute force attacks. Many available graphical passwords have a password space that is less than or equal to the textual password space. Smart cards or tokens can be stolen. Many biometric authentications have been proposed; however, users tend to resist using biometrics because of their intrusiveness and the effect on their privacy. Moreover, biometrics cannot be revoked. In this paper, we present and evaluate our contribution, i.e., the 3-D password. The 3-D password is a multifactor authentication scheme. To be authenticated, we present a 3-D virtual environment where the user navigates and interacts with various objects. The sequence of actions and interactions toward the objects inside the 3-D environment constructs the users 3-D password. The 3-D password can combine most existing authentication schemes such as textual passwords, graphical passwords, and various types of biometrics into a 3-D virtual environment. The design of the 3-D virtual environment and the type of objects selected determine the 3-D password key space.

A Novel 3D Graphical Password Schema

In this paper, we propose and evaluate our contribution which is a new scheme of authentication. This scheme is based on a virtual three-dimensional environment. Users navigate through the virtual environment and interact with items inside the virtual three-dimensional environment. The combination of all interactions, actions and inputs towards the items and towards the virtual three-dimensional environment constructs the users 3D password. The 3D password combines most existing authentication schemes such as textual passwords, graphical passwords, and biometrics into one virtual three-dimensional environment. The 3D passwords main application is the protection of critical resources and systems

Feature selection and classification in genetic programming: Application to haptic-based biometric data

In this paper, a study is conducted in order to explore the use of genetic programming, in particular gene expression programming (GEP), in finding analytic functions that can behave as classifiers in high-dimensional haptic feature spaces. More importantly, the determined explicit functions are used in discovering minimal knowledge-preserving subsets of features from very high dimensional haptic datasets, thus acting as general dimensionality reducers. This approach is applied to the haptic-based biometrics problem; namely, in user identity verification. GEP models are initially generated using the original haptic biometric datatset, which is imbalanced in terms of the number of representative instances of each class. This procedure was repeated while considering an under-sampled (balanced) version of the datasets. The results demonstrated that for all datasets, whether imbalanced or under-sampled, a certain number (on average) of perfect classification models were determined. In addition, using GEP, great feature reduction was achieved as the generated analytic functions (classifiers) exploited only a small fraction of the available features.

Threshold-based Collaborative Access Control (T-CAC)

Userpsilas information confidentiality and privacy rise with the increased usage of computers by various organizations. Abuse of privileges might occur when the trust is based on a single person. Moreover, access control models usually consider only regular policies and are not appropriate to unusual or exceptional circumstances. For instance, in case of emergencies many users might override the access control system in order to access unauthorized sensitive data which is undesirable.

Feature selection in haptic-based handwritten signatures using rough sets

This paper explores the use of rough set theory for feature selection in high dimensional haptic-based handwritten signatures (exploited for user identification). Two rough set-based methods for feature selection are analyzed, the first is a greedy approach while the second relies on genetic algorithms to find minimal subsets of attributes. Also, to further reduce the haptic feature space while maximizing user identification accuracy, a method is proposed where feature vectors are subsampled prior to the feature selection procedure. Rough setgenerated minimal subsets are initially exploited to determine the importance of different haptic data types (e.g. force, position, torque and orientation) in discriminating between different users. In addition, a comparison between rough set-based methods and classical machine learning techniques in the selection of minimal information-preserving subsets of features in high dimensional haptic datasets, is provided. The criteria for comparison are the length of the selected subsets of features and their corresponding discrimination power. Support Vector Machine classifiers are used to evaluate the accuracy of the selected minimal feature vectors. The results demonstrated that the combination of rough set and genetic algorithm techniques can outperform well-established machine learning methods in the selection of minimal subsets of features present in haptic-based handwritten signatures.

Exploring the underlying structure of haptic-based handwritten signatures using visual data mining techniques

In this paper, multidimensional and time-varying haptic-based handwritten signatures are analyzed within a visual data mining paradigm while relying on unsupervised construction of virtual reality spaces using classical optimization and genetic programming. Specifically, the suggested approaches make use of nonlinear transformations to map a high dimensional feature space into another space of smaller dimension while minimizing some error measure of information loss. A comparison between genetic programming and classical optimization techniques in the construction of visual spaces using large haptic datasets, is provided. In addition, different distance functions (used in the nonlinear mapping procedure between the original and visual spaces) are examined to explore whether the choice of measure affects the representation accuracy of the computed visual spaces. Furthermore, different classifiers (Support Vector Machines (SVM), k-nearest neighbors (k-NN), and Nai¿ve Bayes) are exploited in order to evaluate the potential discrimination power of the generated attributes. The results show that the relationships between the haptic data objects and their classes can be appreciated in most of the obtained spaces regardless of the mapping error. Also, spaces computed using classical optimization resulted in lower mapping errors and better discrimination power than genetic programming, but the later provides explicit equations relating the original and the new spaces.

Characterizing biometric behavior through haptics and Virtual Reality

Haptics technology allows users to interact via the sense of touch by applying forces, vibrations and/or motions to users. With this technology, particularly by using force-feedback haptic devices (like stylus-based haptic devices), data directly generated by the user as he/she interacts with a system can be recorded and used-feature space- for authentication purposes. In this paper, nonlinear transformations are applied to the original feature space in order to produce Euclidean 3D spaces preserving the similarity structure of the samples, which are represented with Virtual Reality (VR) techniques. By using these new spaces, it is visualized how certain features (i.e. position, pressure and torque) contain more meaningful information that can characterize a biometric profile when virtually signing.

Identity verification based on handwritten signatures with haptic information using genetic programming

In this article, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including support vector machine, k-nearest neighbors, naïve Bayes, and random forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high-dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favorably with the classical methods and use a much fewer number of attributes (with simple function sets).

Revocable handwritten signatures with haptic information

With recent advances in both the hardware and software for three dimensional applications, virtual environments are growing in popularity. Haptic devices offer a more immersed interaction between users and the virtual environments; and with the growing use of the internet to connect more users, arises the need to authenticate their identity in a secure manner. Identifying users by their interaction with haptic devices is an emerging research field that is proving promising, but like all biometric based authentications relies on the uniqueness of the stored template, which poses a risk if the template is compromised, because unlike passwords and pins, biometric templates are irrevocable. In this paper we explore different ways to introduce revocability to biometric templates with haptic information which are used for user authentication.

Deducing user's fatigue from haptic data

Undesired physical fatigue reduces the overall Quality of Experience (QoE) of virtual reality haptics applications. Detecting fatigue is the first step in rectifying this problem. Fatigue in usability analysis is usually detected through conducting questionnaires and observations. This paper introduces an objective indirect discovery of users fatigue through analyzing data of a haptic writing application. Our results show that if users are feeling tired their kinetic energy would decrease. We can compute this kinetic energy from the velocity of the arm movement during the usage of the haptic device.