The security of machine learning

Machine learning’s ability to rapidly evolve to changing and complex situations has helped it become a fundamental tool for computer security. That adaptability is also a vulnerability: attackers can exploit machine learning systems. We present a taxonomy identifying and analyzing attacks against machine learning systems. We show how these classes influence the costs for the attacker and defender, and we give a formal structure defining their interaction. We use our framework to survey and analyze the literature of attacks against machine learning systems. We also illustrate our taxonomy by showing how it can guide attacks against SpamBayes, a popular statistical spam filter. Finally, we discuss how our…

• NS
  National Science Foundation

  Awards: CCF-0424422, 0424422
• UD
  U.S. Department of Homeland Security
• CS
  Cisco Systems
• AW
  Amazon Web Services
• N
  NetApp
• V
  VMware
• AR
  Advanced Research Projects Agency
• AF
  Air Force Office of Scientific Research

  Awards: FA9550-, FA9550-06-1-0244, FA9550
• OR
  Oak Ridge National Laboratory