Team for Research in
Ubiquitous Secure Technology

Evaluation of Classifiers used in Security Applications
Alvaro Cardenas

Citation
Alvaro Cardenas. "Evaluation of Classifiers used in Security Applications". Talk or presentation, 24, May, 2006.

Abstract
In recent years several tools based on statistical methods and machine learning have been incorporated in security related tasks involving classification, such as intrusion detection systems, fraud detection, spam filters, biometrics and multimedia forensics. Measuring the security performance of these classifiers is an essential part for facilitating decision making, determining the viability of the product, and providing the insight necessary to improve the design of the system. There are however relevant considerations for security related problems that are sometimes ignored by traditional evaluation schemes. The first consideration is the usually large class imbalance between normal events and attack events. The second consideration is the fact that the classifier or learning rule will be deployed in an adversarial environment. In this talk we introduce the Bayesian-ROC curves for the class imbalance problem and provide a framework to evaluate the performance under the worst type of adversarial attacks. We provide practical examples in intrusion detection and multimedia watermarking.

Electronic downloads

Citation formats  
  • HTML
     Alvaro Cardenas. <a
    href="http://www.truststc.org/pubs/102.html"
    ><i>Evaluation of Classifiers used in Security
    Applications</i></a>, Talk or presentation,  24,
    May, 2006.
  • Plain text
     Alvaro Cardenas. "Evaluation of Classifiers used in
    Security Applications". Talk or presentation,  24, May,
    2006.
  • BibTeX
    @presentation{Cardenas06_EvaluationOfClassifiersUsedInSecurityApplications,
        author = { Alvaro Cardenas},
        title = {Evaluation of Classifiers used in Security
                  Applications},
        day = {24},
        month = {May},
        year = {2006},
        abstract = {In recent years several tools based on statistical
                  methods and machine learning have been
                  incorporated in security related tasks involving
                  classification, such as intrusion detection
                  systems, fraud detection, spam filters, biometrics
                  and multimedia forensics. Measuring the security
                  performance of these classifiers is an essential
                  part for facilitating decision making, determining
                  the viability of the product, and providing the
                  insight necessary to improve the design of the
                  system. There are however relevant considerations
                  for security related problems that are sometimes
                  ignored by traditional evaluation schemes. The
                  first consideration is the usually large class
                  imbalance between normal events and attack events.
                  The second consideration is the fact that the
                  classifier or learning rule will be deployed in an
                  adversarial environment. In this talk we introduce
                  the Bayesian-ROC curves for the class imbalance
                  problem and provide a framework to evaluate the
                  performance under the worst type of adversarial
                  attacks. We provide practical examples in
                  intrusion detection and multimedia watermarking.},
        URL = {http://www.truststc.org/pubs/102.html}
    }
    

Posted by Christopher Brooks on 2 Jun 2006.
Groups: trustseminar
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