The Experts below are selected from a list of 3006 Experts worldwide ranked by ideXlab platform

Ahmed Awad E. Ahmed - One of the best experts on this subject based on the ideXlab platform.

  • Dynamic Sample Size Detection in Learning Command Line Sequence for Continuous Authentication
    IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2012
    Co-Authors: Issa Traore, Youssef Nakkabi, Ahmed Awad E. Ahmed, Isaac Woungang, Mohammad S. Obaidat, Bijan Khalilian
    Abstract:

    Continuous authentication (CA) consists of authenticating the user repetitively throughout a session with the goal of detecting and protecting against session hijacking attacks. While the accuracy of the detector is central to the success of CA, the detection delay or length of an individual authentication period is important as well since it is a measure of the window of vulnerability of the system. However, high accuracy and small detection delay are conflicting requirements that need to be balanced for optimum detection. In this paper, we propose the use of sequential sampling technique to achieve optimum detection by trading off adequately between detection delay and accuracy in the CA process. We illustrate our approach through CA based on user command line sequence and naïve Bayes classification scheme. Experimental evaluation using the Greenberg data set yields encouraging results consisting of a false acceptance rate (FAR) of 11.78% and a false rejection rate (FRR) of 1.33%, with an average command sequence length (i.e., detection delay) of 37 commands. When using the Schonlau (SEA) data set, we obtain FAR = 4.28% and FRR = 12%.

  • Improving Mouse Dynamics Biometric Performance Using Variance Reduction via Extractors With Separate Features
    IEEE Transactions on Systems Man and Cybernetics - Part A: Systems and Humans, 2010
    Co-Authors: Youssef Nakkabi, Issa Traore, Ahmed Awad E. Ahmed
    Abstract:

    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%.

X U Mingxing - One of the best experts on this subject based on the ideXlab platform.

  • using word confidence measure for oov words detection in a spontaneous spoken dialog system
    Conference of the International Speech Communication Association, 2003
    Co-Authors: Guoliang Zhang, Fang Zheng, X U Mingxing
    Abstract:

    Developing a real-life spoken dialogue system must face with many practical issues, where the out-of-vocabulary (OOV) words problem is one of the key difficulties. This paper presents the OOV detection mechanism based on the word confidence scoring developed for the d-Ear Attendant system, a spontaneous spoken dialogue system. In the d-Ear Attendant system, an explicit filler model is originally used to detect the presence of OOV words [1]. Although this approach has a satisfactory OOV detection rate, it badly degrades the accuracy of in-vocabulary (IV) detection by 4.4% absolutely (from 97% to 92.6%). Such the degradation will not be acceptable in a practical system. By using a few commonly used acoustic confidence features and some new context confidence features, our confidence measure method not only is able to detect the word level speech recognition errors, but also has a good ability for OOV words detection with an acceptable false alarm rate. For example, with a false rejection rate of 2.5%, the false acceptance rate of 26% is achieved.

Jia Liu - One of the best experts on this subject based on the ideXlab platform.

  • confidence score based unsupervised incremental adaptation for oov words detection
    Lecture Notes in Computer Science, 2006
    Co-Authors: Wei Chu, Xi Xiao, Jia Liu
    Abstract:

    This paper presents a novel approach of distinguishing in-vocabulary (IV) words and out-of-vocabulary (OOV) words by using confidence score-based unsupervised incremental adaptation. The unsupervised adaptation uses Viterbi decode results which have high confidence scores to adjust new acoustic models. The adjusted acoustic models can award IV words and punish OOV words in confidence score, thus obtain the goal of separating IV and OOV words. Our Automatic Speech Recognition Laboratory has developed a Speech Recognition Developer Kit (SRDK) which serves as a baseline system for different speech recognition tasks. Experiments conducted on the SRDK system have proved that this method can achieve a rise over 41% in OOV words detection rate (from 68% to 96%) at the same cost of a false alarm (taken IV words as OOV words) rate of 10%. This method also obtains a rise over 11% in correct acceptance rate (from 88% to 98%) at the same cost of a false acceptance rate of 20%.

Junichi Yamagishi - One of the best experts on this subject based on the ideXlab platform.

  • revisiting the security of speaker verification systems against imposture using synthetic speech
    International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Phillip L. De Leon, Michael Pucher, Vijendra Raj Apsingekar, Junichi Yamagishi
    Abstract:

    In this paper, we investigate imposture using synthetic speech. Although this problem was first examined over a decade ago, dramatic improvements in both speaker verification (SV) and speech synthesis have renewed interest in this problem. We use a HMM-based speech synthesizer which creates synthetic speech for a targeted speaker through adaptation of a background model. We use two SV systems: standard GMM-UBM-based and a newer SVM-based. Our results show when the systems are tested with human speech, there are zero false acceptances and zero false rejections. However, when the systems are tested with synthesized speech, all claims for the targeted speaker are accepted while all other claims are rejected. We propose a two-step process for detection of synthesized speech in order to prevent this imposture. Overall, while SV systems have impressive accuracy, even with the proposed detector, high-quality synthetic speech will lead to an unacceptably high false acceptance rate.

Sungzoon Cho - One of the best experts on this subject based on the ideXlab platform.

  • keystroke dynamics identity verification its problems and practical solutions
    Computers & Security, 2004
    Co-Authors: Sungzoon Cho
    Abstract:

    Password is the most widely used identity verification method in computer security domain. However, because of its simplicity, it is vulnerable to imposter attacks. Use of keystroke dynamics can result in a more secure verification system. Recently, Cho et al. (J Organ Comput Electron Commerce 10 (2000) 295) proposed autoassociative neural network approach, which used only the user's typing patterns, yet reporting a low error rate: 1.0% false rejection rate (FRR) and 0% false acceptance rate (FAR). However, the previous research had some limitations: (1) it took too long to train the model; (2) data were preprocessed subjectively by a human; and (3) a large data set was required. In this article, we propose the corresponding solutions for these limitations with an SVM novelty detector, GA-SVM wrapper feature subset selection, and an ensemble creation based on feature selection, respectively. Experimental results show that the proposed methods are promising, and that the keystroke dynamics is a viable and practical way to add more security to identity verification.