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Related Experiment Videos

Randomized Prediction Games for Adversarial Machine Learning.

Samuel Rota Bulo1, Battista Biggio2, Ignazio Pillai2

  • 1ICT-Tev, Fondazione Bruno Kessler, Trento, Italy.

IEEE Transactions on Neural Networks and Learning Systems
|August 12, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a randomized prediction game to enhance machine learning security against evasion attacks. The new game-theoretic approach improves the balance between detecting threats and reducing false alarms in applications like spam and malware detection.

Keywords:
Algorithm design and analysisCost functionGamesGeneratorsMalwareSecurityTraining

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Area of Science:

  • Machine Learning Security
  • Game Theory
  • Cybersecurity

Background:

  • Attackers use randomization to evade detection in spam and malware.
  • Existing secure learning algorithms often lack randomization, making them vulnerable.
  • Game-theoretical approaches for secure classifiers have been deterministic.

Purpose of the Study:

  • To introduce a randomized prediction game for more robust machine learning security.
  • To address the limitations of deterministic models in secure classifier design.
  • To improve the trade-off between detection rates and false alarms.

Main Methods:

  • Proposed a noncooperative game-theoretic formulation with randomized strategies for both classifier and attacker.
  • Modeled randomized strategy selections based on probability distributions.
  • Evaluated the approach on handwritten digit recognition, spam, and malware detection datasets.

Main Results:

  • The randomized prediction game significantly improves the detection-false alarm trade-off compared to state-of-the-art secure classifiers.
  • The proposed method demonstrates effectiveness even against unforeseen attack variations.
  • Enhanced robustness against evasion attacks in real-world applications.

Conclusions:

  • Randomized game-theoretic formulations offer a more effective approach to secure machine learning.
  • The proposed method provides improved resilience against sophisticated evasion tactics.
  • This work advances the field of adversarial machine learning by incorporating randomization.