Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371)

Authors Anthony D. Joseph, Pavel Laskov, Fabio Roli, J. Doug Tygar, Blaine Nelson and all authors of the abstracts in this report



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Author Details

Anthony D. Joseph
Pavel Laskov
Fabio Roli
J. Doug Tygar
Blaine Nelson
and all authors of the abstracts in this report

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Anthony D. Joseph, Pavel Laskov, Fabio Roli, J. Doug Tygar, and Blaine Nelson. Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371). In Dagstuhl Reports, Volume 2, Issue 9, pp. 109-130, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)
https://doi.org/10.4230/DagRep.2.9.109

Abstract

The study of learning in adversarial environments is an emerging discipline at the juncture between machine learning and computer security that raises new questions within both fields. The interest in learning-based methods for security and system design applications comes from the high degree of complexity of phenomena underlying the security and reliability of computer systems. As it becomes increasingly difficult to reach the desired properties by design alone, learning methods are being used to obtain a better understanding of various data collected from these complex systems. However, learning approaches can be co-opted or evaded by adversaries, who change to counter them. To-date, there has been limited research into learning techniques that are resilient to attacks with provable robustness guarantees making the task of designing secure learning-based systems a lucrative open research area with many challenges. The Perspectives Workshop, ``Machine Learning Methods for Computer Security'' was convened to bring together interested researchers from both the computer security and machine learning communities to discuss techniques, challenges, and future research directions for secure learning and learning-based security applications. This workshop featured twenty-two invited talks from leading researchers within the secure learning community covering topics in adversarial learning, game-theoretic learning, collective classification, privacy-preserving learning, security evaluation metrics, digital forensics, authorship identification, adversarial advertisement detection, learning for offensive security, and data sanitization. The workshop also featured workgroup sessions organized into three topic: machine learning for computer security, secure learning, and future applications of secure learning.
Keywords
  • Adversarial Learning
  • Computer Security
  • Robust Statistical Learning
  • Online Learning with Experts
  • Game Theory
  • Learning Theory

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