Machine learning in adversarial environments

@article{Laskov2010MachineLI,
  title={Machine learning in adversarial environments},
  author={P. Laskov and R. Lippmann},
  journal={Machine Learning},
  year={2010},
  volume={81},
  pages={115-119}
}
Whenever machine learning is used to prevent illegal or unsanctioned activity and there is an economic incentive, adversaries will attempt to circumvent the protection provided. Constraints on how adversaries can manipulate training and test data for classifiers used to detect suspicious behavior make problems in this area tractable and interesting. This special issue highlights papers that span many disciplines including email spam detection, computer intrusion detection, and detection of web… Expand
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101 Citations
Highly Influential Citations
1
Background Citations
48
Methods Citations
9

101 Citations

Citation Type

Citation Type

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Adding Robustness to Support Vector Machines Against Adversarial Reverse Engineering
  • 28
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Study of Evasion Attack using Feature Selection in Adversarial Environment
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Adversarial Machine Learning Challenges
Active learning intrusion detection using k-means clustering selection
  • 12
Bagging Classifiers for Fighting Poisoning Attacks in Adversarial Classification Tasks
  • 83
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A Machine Learning Techinque For Generative Classifier Under Attack
Feature Cross-Substitution in Adversarial Classification
  • 88
  • PDF
Vulnerability Detection and Analysis in Adversarial Deep Learning
  • 13
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