ETRI Journal

  • 제41권5호
  • /
  • Pages.608-618
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  • 2019
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Keyed learning: An adversarial learning framework-formalization, challenges, and anomaly detection applications

  • 투고 : 2019.03.20
  • 심사 : 2019.08.28
  • 발행 : 2019.10.01
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초록

We propose a general framework for keyed learning, where a secret key is used as an additional input of an adversarial learning system. We also define models and formal challenges for an adversary who knows the learning algorithm and its input data but has no access to the key value. This adversarial learning framework is subsequently applied to a more specific context of anomaly detection, where the secret key finds additional practical uses and guides the entire learning and alarm-generating procedure.

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참고문헌

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