Near-Optimal Evasion of Convex-Inducing Classifiers
2010
Classifiers are often used to detect miscreant activities. We study how an adversary can efficiently query a classifier to elicit information that allows the adversary to evade detection at near-minimal cost. We generalize results of Lowd and Meek (2005) to convex-inducing classifiers. We present algorithms that construct undetected instances of near-minimal cost using only polynomially many queries in the dimension of the space and without reverse engineering the decision boundary.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
13
References
26
Citations
NaN
KQI