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.
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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.
Supported in partVerified against the work's full text
Sethi and Kantardzic show adversaries deliberately induce drift to evade classifiers and detectors, but do not frame it as outpacing defenders' retraining.
“the drift is intended to degrade the system and at the same time designed to avoid detection by traditional concept drift detection techniques”
- In fraud and security detection, the adversary's whole job is to drift the concept faster than defenders can retrain.
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