The Security of Machine Learning

Abstract

Machine learning has become a fundamental tool for computer security, since it can rapidly evolve to changing and complex situations. 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 taxonomy suggests new lines of defenses.

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Document Details

Document Type
Technical Report
Publication Date
Apr 24, 2008
Accession Number
ADA519143

Entities

People

  • Anthony D. Joseph
  • Blaine A. Nelson
  • Doug Tygar
  • Marco Barreno

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Autonomy
  • Cyber
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computer Science
  • Cybersecurity
  • Data Mining
  • Detection
  • Detectors
  • Electronic Mail
  • Engineering
  • Information Science
  • Information Theory
  • Intrusion Detection
  • Intrusion Detection Systems
  • Intrusion Detectors
  • Machine Learning
  • Security
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Cybersecurity.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Cyber