생체의 모델링과 Simulation

We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen. The classifier achieves a median equal error rate of 0% for intrasession authentication, 2%-3% for intersession authentication, and below 4% when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multimodal biometric authentication system.

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Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication

Dependence on computers to store and process sensitive information has made it necessary to secure them from intruders. A behavioral biometric such as keystroke dynamics which makes use of the typing cadence of an individual can be used to strengthen existing security techniques effectively and cheaply. Due to the ballistic (semi-autonomous) nature of the typing behavior it is difficult to impersonate, making it useful as abiometric. Therefore in this paper, we provide a basic background of the psychological basis behind the use of keystroke dynamics. We also discuss the data acquisition methods, approaches and the performance of the methods used by researchers on standard computer keyboards. In this survey, we find that the use and acceptance of this biometric could be increased by development of standardized databases, assignment of nomenclature for features, development of common data interchange formats, establishment of protocols for evaluating methods, and resolution of privacy issues.

Biometric Authentication and Identification using Keystroke Dynamics: A Survey

In order to direct and build an effective, secure mobile ecosystem, we must first understand user attitudes toward security and privacy for smartphones and how they may differ from attitudes toward more traditional computing systems. What are users' comfort levels in performing different tasks? How do users select applications? What are their overall perceptions of the platform? This understanding will help inform the design of more secure smartphones that will enable users to safely and confidently benefit from the potential and convenience offered by mobile platforms.To gain insight into user perceptions of smartphone security and installation habits, we conduct a user study involving 60 smartphone users. First, we interview users about their willingness to perform certain tasks on their smartphones to test the hypothesis that people currently avoid using their phones due to privacy and security concerns. Second, we analyze why and how they select applications, which provides information about how users decide to trust applications. Based on our findings, we present recommendations and opportunities for services that will help users safely and confidently use mobile applications and platforms.

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Measuring user confidence in smartphone security and privacy

This study is a survey and classification of the state-of-the-art in behavioural biometrics which is based on skills, style, preference, knowledge, motor-skills or strategy used by people while accomplishing different everyday tasks such as driving an automobile, talking on the phone or using a computer. The authors examine current research in the field and analyse the types of features used to describe different types of behaviour. After comparing accuracy rates for verification of users using different behavioural biometric approaches, researchers address privacy issues which arise or might arise in the future with the use of behavioural biometrics.

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Behavioural biometrics: a survey and classification

In this paper, we introduce a new form of behavioral biometrics based on mouse dynamics, which can be used in different security applications. We develop a technique that can be used to model the behavioral characteristics from the captured data using artificial neural networks. In addition, we present an architecture and implementation for the detector, which cover all the phases of the biometric data flow including the detection process. Experimental data illustrating the experiments conducted to evaluate the accuracy of the proposed detection technique are presented and analyzed. Specifically, three series of experiments are conducted. The main experiment, in which 22 participants are involved, reproduces real operating conditions in computing systems by giving participants an individual choice of operating environments and applications; 284 hours of raw mouse data are collected over 998 sessions, with an average of 45 sessions per user. The two other experiments, involving seven participants, provided a basis for studying the confounding factors arising from the main experiment by fixing the environment variables. In the main experiment, the performance results presented using receiver operating characteristic (ROC) curves and a confusion matrix yield at the crossover point (that is, the threshold set for an equal error rate) a false acceptance rate (FAR) of 2.4649 percent and a false rejection rate (FRR) of 2.4614 percent.

A New Biometric Technology Based on Mouse Dynamics

The present invention provides a system and methods for computer user profiling based on behavioral biometrics. The approach consists of establishing distinctive profiles for computer users based on how they use a motion-based input device such as, but not limited to, a mouse and/or a keyboard. The profiles computed in the present invention are more accurate than those obtained through the traditional statistical profiling techniques, since they are based on distinctive biological characteristics of users.

System and method for determining a computer user profile from a motion-based input device

Accurate recognition of free text keystroke dynamics is challenging due to the unstructured and sparse nature of the data and its underlying variability As a result, most of the approaches published in the literature on free text recognition, except for one recent one, have reported extremely high error rates In this paper, we present a new approach for the free text analysis of keystrokes that combines monograph and digraph analysis, and uses a neural network to predict missing digraphs based on the relation between the monitored keystrokes Our proposed approach achieves an accuracy level comparable to the best results obtained through related techniques in the literature, while achieving a far lower processing time Experimental evaluation involving 53 users in a heterogeneous environment yields a false acceptance ratio (FAR) of 00152% and a false rejection ratio (FRR) of 482%, at an equal error rate (EER) of 246% Our follow-up experiment, in a homogeneous environment with 17 users, yields FAR=0% and FRR=501%, at EER=213%

Biometric Recognition Based on Free-Text Keystroke Dynamics

The European standard for access control imposes stringent performance requirements on commercial biometric technologies that few existing recognition systems are able to meet. In this correspondence paper, we present the first mouse dynamics biometric recognition system that fulfills this standard. The proposed system achieves notable performance improvement by developing separate models for separate feature groups involved. The improvements are achieved through the use of a fuzzy classification based on the Learning Algorithm for Multivariate Data Analysis and using a score-level fusion scheme to merge corresponding biometric scores. Evaluation of the proposed framework using mouse data from 48 users achieves a false acceptance rate of 0% and a false rejection rate of 0.36%.

Improving Mouse Dynamics Biometric Performance Using Variance Reduction via Extractors With Separate Features

In this work we introduce the idea of using behavioral biometrics in intrusion detection applications. We propose a new approach to user profiling, which can be used to detect intrusion without the need for any special hardware implementation and without forcing the user to perform any special actions. The technique is based on using "keystroke dynamics" and "mouse dynamics" biometrics. The profiles computed in this case are more accurate than those obtained through the traditional statistical profiling techniques, since they are based on distinctive biological characteristics of users.

Ahmed Awad E. Ahmed

Papers

A New Biometric Technology Based on Mouse Dynamics

System and method for determining a computer user profile from a motion-based input device

Biometric Recognition Based on Free-Text Keystroke Dynamics

Improving Mouse Dynamics Biometric Performance Using Variance Reduction via Extractors With Separate Features

Anomaly intrusion detection based on biometrics