The Experts below are selected from a list of 1755 Experts worldwide ranked by ideXlab platform

Patrick Bours - One of the best experts on this subject based on the ideXlab platform.

  • a study on continuous authentication using a combination of keystroke and mouse Biometrics
    Neurocomputing, 2017
    Co-Authors: Soumik Mondal, Patrick Bours
    Abstract:

    In this paper we focus on a context independent continuous authentication system that reacts on every separate action performed by a user. We contribute with a robust dynamic trust model algorithm that can be applied to any continuous authentication system, irrespective of the Biometric Modality. We also contribute a novel performance reporting technique for continuous authentication. Our proposed approach was validated with extensive experiments with a unique behavioural Biometric dataset. This dataset was collected under complete uncontrolled condition from 53 users by using our data collection software. We considered both keystroke and mouse usage behaviour patterns to prevent a situation where an attacker avoids detection by restricting to one input device because the system only checks the other input device. During our research, we developed a feature selection technique that could be applied to other pattern recognition problems.The best result obtained in this research is that 50 out of 53 genuine users are never inadvertently locked out by the system, while the remaining 3 genuine users (i. e. 5.7%) are sometimes locked out, on average after 2265 actions. Furthermore, there are only 3 out of 2756 impostors not been detected, i.e. only 0.1% of the impostors go undetected. Impostors are detected on average after 252 actions.

  • Keystroke dynamics performance enhancement with soft Biometrics
    2015 IEEE International Conference on Identity Security and Behavior Analysis ISBA 2015, 2015
    Co-Authors: Syed Zulkarnain Syed Idrus, Christina Rosenberger, Estelle Cherrier, Soumik Mondal, Patrick Bours
    Abstract:

    It is accepted that the way a person types on a keyboard contains timing patterns, which can be used to classify him/her, is known as keystroke dynamics. Keystroke dynamics is a behavioural Biometric Modality, whose performances, however, are worse than morphological modalities such as fingerprint, iris recognition or face recognition. To cope with this, we propose to combine keystroke dynamics with soft Biometrics. Soft Biometrics refers to Biometric characteristics that are not sufficient to authenticate a user (e.g. height, gender, skin/eye/hair colour). Concerning keystroke dynamics, three soft categories are considered: gender, age and handedness. We present different methods to combine the results of a classical keystroke dynamics system with such soft criteria. By applying simple sum and multiply rules, our experiments suggest that the combination approach performs better than the classification approach with best result of 5.41% of equal error rate. The efficiency of our approaches is illustrated on a public database.

  • Soft Biometrics for keystroke dynamics: Profiling individuals while typing passwords
    Computers & Security, 2014
    Co-Authors: Syed Zulkarnain Syed Idrus, Estelle Cherrier, Christophe Rosenberger, Patrick Bours
    Abstract:

    This paper presents a new profiling approach of individuals based on soft Biometrics for keystroke dynamics. Soft Biometric traits are unique representation of a person, which can be in a form of physical, behavioural or biological human characteristics that differentiate between him/her into a group people (e.g. gender, age, height, colour, race etc.). Keystroke dynamics is a behavioural Biometric Modality to recognise how a person types on a keyboard. In this paper, we consider the following soft traits: the hand category (i.e. if the user types with one or two hands), the gender category, the age category and the handedness category. For this purpose, we collected a new database. Two cases are studied: static passwords and free text. By combining machine learning and fusion process, the results are promising.

  • Soft Biometrics database: A benchmark for keystroke dynamics Biometric systems
    Biometrics Special Interest Group (BIOSIG) 2013 International Conference of the, 2013
    Co-Authors: Syed Zulkarnain Syed Idrus, Christina Rosenberger, Estelle Cherrier, Patrick Bours
    Abstract:

    Among all the existing Biometric modalities, authentication systems based on keystroke dynamics are particularly interesting for usability reasons. Many researchers proposed in the last decades some algorithms to increase the efficiency of this Biometric Modality. Propose in this paper: a benchmark testing suite composed of a database containing multiple data (keystroke dynamics templates, soft Biometric traits ...), which will be made available for the research community and a software that is already available for the scientific community for the evaluation of keystroke dynamics based systems. We also built the proposed Biometric database on soft Biometric traits for keystroke dynamics to suit the experiment. 110 people had voluntarily participated and gave their soft Biometrics data i.e. the way of typing gender age and handedness.

Christophe Rosenberger - One of the best experts on this subject based on the ideXlab platform.

  • Statistical Modeling of Keystroke Dynamics Samples For the Generation of Synthetic Datasets
    Future Generation Computer Systems, 2019
    Co-Authors: Denis Migdal, Christophe Rosenberger
    Abstract:

    Biometrics is an emerging technology more and more present in our daily life. However, building Biometric systems requires a large amount of data that may be difficult to collect. Collecting such sensitive data is also very time consuming and constrained, s.a. GDPR legislation in Europe. In the case of keystroke dynamics, most existing databases have less than 200 users. For these reasons, it is crucial for this Biometric Modality to be able to generate a significant and realistic synthetic dataset of keystroke dynamics samples. We propose in this paper an original approach for the generation of synthetic keystroke data given samples from known users as a first step towards the generation of synthetic datasets. Experimental results show the capability of the proposed statistical model to generate realistic samples from existing datasets in the literature.

  • Soft Biometrics for keystroke dynamics: Profiling individuals while typing passwords
    Computers & Security, 2014
    Co-Authors: Syed Zulkarnain Syed Idrus, Estelle Cherrier, Christophe Rosenberger, Patrick Bours
    Abstract:

    This paper presents a new profiling approach of individuals based on soft Biometrics for keystroke dynamics. Soft Biometric traits are unique representation of a person, which can be in a form of physical, behavioural or biological human characteristics that differentiate between him/her into a group people (e.g. gender, age, height, colour, race etc.). Keystroke dynamics is a behavioural Biometric Modality to recognise how a person types on a keyboard. In this paper, we consider the following soft traits: the hand category (i.e. if the user types with one or two hands), the gender category, the age category and the handedness category. For this purpose, we collected a new database. Two cases are studied: static passwords and free text. By combining machine learning and fusion process, the results are promising.

Syed Zulkarnain Syed Idrus - One of the best experts on this subject based on the ideXlab platform.

  • Soft Biometrics for keystroke dynamics: Profiling individuals while typing passwords
    Computers & Security, 2014
    Co-Authors: Syed Zulkarnain Syed Idrus, Estelle Cherrier, Christophe Rosenberger, Patrick Bours
    Abstract:

    This paper presents a new profiling approach of individuals based on soft Biometrics for keystroke dynamics. Soft Biometric traits are unique representation of a person, which can be in a form of physical, behavioural or biological human characteristics that differentiate between him/her into a group people (e.g. gender, age, height, colour, race etc.). Keystroke dynamics is a behavioural Biometric Modality to recognise how a person types on a keyboard. In this paper, we consider the following soft traits: the hand category (i.e. if the user types with one or two hands), the gender category, the age category and the handedness category. For this purpose, we collected a new database. Two cases are studied: static passwords and free text. By combining machine learning and fusion process, the results are promising.

  • Soft Biometrics database: A benchmark for keystroke dynamics Biometric systems
    Biometrics Special Interest Group (BIOSIG) 2013 International Conference of the, 2013
    Co-Authors: Syed Zulkarnain Syed Idrus, Christina Rosenberger, Estelle Cherrier, Patrick Bours
    Abstract:

    Among all the existing Biometric modalities, authentication systems based on keystroke dynamics are particularly interesting for usability reasons. Many researchers proposed in the last decades some algorithms to increase the efficiency of this Biometric Modality. Propose in this paper: a benchmark testing suite composed of a database containing multiple data (keystroke dynamics templates, soft Biometric traits ...), which will be made available for the research community and a software that is already available for the scientific community for the evaluation of keystroke dynamics based systems. We also built the proposed Biometric database on soft Biometric traits for keystroke dynamics to suit the experiment. 110 people had voluntarily participated and gave their soft Biometrics data i.e. the way of typing gender age and handedness.

Estelle Cherrier - One of the best experts on this subject based on the ideXlab platform.

  • Keystroke dynamics performance enhancement with soft Biometrics
    2015 IEEE International Conference on Identity Security and Behavior Analysis ISBA 2015, 2015
    Co-Authors: Syed Zulkarnain Syed Idrus, Christina Rosenberger, Estelle Cherrier, Soumik Mondal, Patrick Bours
    Abstract:

    It is accepted that the way a person types on a keyboard contains timing patterns, which can be used to classify him/her, is known as keystroke dynamics. Keystroke dynamics is a behavioural Biometric Modality, whose performances, however, are worse than morphological modalities such as fingerprint, iris recognition or face recognition. To cope with this, we propose to combine keystroke dynamics with soft Biometrics. Soft Biometrics refers to Biometric characteristics that are not sufficient to authenticate a user (e.g. height, gender, skin/eye/hair colour). Concerning keystroke dynamics, three soft categories are considered: gender, age and handedness. We present different methods to combine the results of a classical keystroke dynamics system with such soft criteria. By applying simple sum and multiply rules, our experiments suggest that the combination approach performs better than the classification approach with best result of 5.41% of equal error rate. The efficiency of our approaches is illustrated on a public database.

  • Soft Biometrics for keystroke dynamics: Profiling individuals while typing passwords
    Computers & Security, 2014
    Co-Authors: Syed Zulkarnain Syed Idrus, Estelle Cherrier, Christophe Rosenberger, Patrick Bours
    Abstract:

    This paper presents a new profiling approach of individuals based on soft Biometrics for keystroke dynamics. Soft Biometric traits are unique representation of a person, which can be in a form of physical, behavioural or biological human characteristics that differentiate between him/her into a group people (e.g. gender, age, height, colour, race etc.). Keystroke dynamics is a behavioural Biometric Modality to recognise how a person types on a keyboard. In this paper, we consider the following soft traits: the hand category (i.e. if the user types with one or two hands), the gender category, the age category and the handedness category. For this purpose, we collected a new database. Two cases are studied: static passwords and free text. By combining machine learning and fusion process, the results are promising.

  • Soft Biometrics database: A benchmark for keystroke dynamics Biometric systems
    Biometrics Special Interest Group (BIOSIG) 2013 International Conference of the, 2013
    Co-Authors: Syed Zulkarnain Syed Idrus, Christina Rosenberger, Estelle Cherrier, Patrick Bours
    Abstract:

    Among all the existing Biometric modalities, authentication systems based on keystroke dynamics are particularly interesting for usability reasons. Many researchers proposed in the last decades some algorithms to increase the efficiency of this Biometric Modality. Propose in this paper: a benchmark testing suite composed of a database containing multiple data (keystroke dynamics templates, soft Biometric traits ...), which will be made available for the research community and a software that is already available for the scientific community for the evaluation of keystroke dynamics based systems. We also built the proposed Biometric database on soft Biometric traits for keystroke dynamics to suit the experiment. 110 people had voluntarily participated and gave their soft Biometrics data i.e. the way of typing gender age and handedness.

Samy Bengio - One of the best experts on this subject based on the ideXlab platform.

  • database protocols and tools for evaluating score level fusion algorithms in Biometric authentication
    Pattern Recognition, 2006
    Co-Authors: Samy Bengio
    Abstract:

    Fusing the scores of several Biometric systems is a very promising approach to improve the overall system's accuracy. Despite many works in the literature, it is surprising that there is no coordinated effort in making a benchmark database available. It should be noted that fusion in this context consists not only of multimodal fusion, but also intramodal fusion, i.e., fusing systems using the same Biometric Modality but different features, or same features but using different classifiers. Building baseline systems from scratch often prevents researchers from putting more efforts in understanding the fusion problem. This paper describes a database of scores taken from experiments carried out on the XM2VTS face and speaker verification database. It then proposes several fusion protocols and provides some state-of-the-art tools to evaluate the fusion performance.

  • a score level fusion benchmark database for Biometric authentication
    Lecture Notes in Computer Science, 2005
    Co-Authors: Norman Poh, Samy Bengio
    Abstract:

    Fusing the scores of several Biometric systems is a very promising approach to improve the overall system's accuracy. Despite many works in the literature, it is surprising that there is no coordinated effort in making a benchmark database available. It should be noted that fusion in this context consists not only of multimodal fusion, but also intramodal fusion, i.e., fusing systems using the same Biometric Modality but different features, or same features but using different classifiers. Building baseline systems from scratch often prevents researchers from putting more efforts in understanding the fusion problem. This paper describes a database of scores taken from experiments carried out on the XM2VTS face and speaker verification database. It then proposes several fusion protocols and provides some state-of-the-art tools to evaluate the fusion performance.