Handling Adversarial Concept Drift in Streaming Data¶
Sethi, T. S., & Kantardzic, M. (2018). Handling Adversarial Concept Drift in Streaming Data. Expert Systems with Applications, 97, 18-40.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Concept Drift
- In fraud and security detection, the adversary's whole job is to drift the concept faster than defenders can retrain.
This sourceTreats adversarial drift in security/fraud detection, where a malicious actor actively shifts the input–outcome relation to evade the classifier faster than passive retraining can adapt — a distinct design regime from natural drift.
- In fraud and security detection, the adversary's whole job is to drift the concept faster than defenders can retrain.
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