Abstract
Biometrics is becoming an important method for human identification. However, once a biometric pattern is stolen, the user will quickly run out of alternatives and all the applications where the associated biometric pattern is used become insecure. Cancelable biometrics is a solution. However, traditional cancelable biometric methods treat the transformation process and feature extraction process independently. As a result, this kind of cancelable biometric approach would reduce the recognition accuracy. In this paper, we first analyzed the limitations of traditional cancelable biometric methods, and proposed the Key Incorporation Scheme for Cancelable Biometrics approach that could increase the recognition accuracy while achieving “cancelability”. Then we designed the Gabor Descriptor based Cancelable Iris Recognition method that is a practical implementation of the proposed Key Incorporation Scheme. The experimental results demonstrate that our proposed method can significantly improve the iris recognition accuracy while achieving “cancelability”.
Highlights
Intrusion detection is an important area in the field of computers and security, and in the recent years it has generated considerable interest in the research community
In this paper we introduced Tanimoto based similarity measure for host-based intrusions using binary feature set for training and classification
In order to detect the deviation of anomalous system call sequences from the normal set of sequences, Liao and Vemuri [5] used a similarity measure based on the frequencies of system calls used by a program, rather than the temporal ordering
Summary
Intrusion detection is an important area in the field of computers and security, and in the recent years it has generated considerable interest in the research community. Host-based anomaly detection systems mostly focus on system call sequences with the assumption that a malicious activity results in an abnormal trace Such data can be collected by logging the system calls using operating system utilities e.g. Linux strace or Solaris Basic Security Module (BSM). Though anomaly-based IDS can detect unknown attacks, it suffers from having unacceptable false-positive rate [1] This is because of the fact that it is hard to perfectly model a normal behavior. A lot of research activities in anomaly detection focus on learning process behaviors and building the profiles with system call sequences as data sources. In carrying out the classification, it is a common practice to use features represented as frequency of system calls observed While this approach has produced outstanding results [7], we are more interested in reducing the computational cost associated with classification task. To the best of authors’ knowledge the result is better than other binary similarity schemes for intrusion detection reported in literature
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
