Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
WSC '96 Proceedings of the 28th conference on Winter simulation
Mimicry attacks on host-based intrusion detection systems
Proceedings of the 9th ACM conference on Computer and communications security
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
Machine Learning
Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Simulated Annealing in Convex Bodies and an 0*(n4) Volume Algorithm
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Convex Optimization
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Solving convex programs by random walks
Journal of the ACM (JACM)
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Diagnosing network-wide traffic anomalies
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series)
Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
On The Power of Membership Queries in Agnostic Learning
The Journal of Machine Learning Research
Analysis of Perceptron-Based Active Learning
The Journal of Machine Learning Research
Undermining an anomaly-based intrusion detection system using common exploits
RAID'02 Proceedings of the 5th international conference on Recent advances in intrusion detection
Classifier evasion: models and open problems
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
Anagram: a content anomaly detector resistant to mimicry attack
RAID'06 Proceedings of the 9th international conference on Recent Advances in Intrusion Detection
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Classifiers are often used to detect miscreant activities. We study how an adversary can systematically query a classifier to elicit information that allows the attacker to evade detection while incurring a near-minimal cost of modifying their intended malfeasance. We generalize the theory of Lowd and Meek (2005) to the family of convex-inducing classifiers that partition their feature space into two sets, one of which is convex. We present query algorithms for this family that construct undetected instances of approximately minimal cost using only polynomially-many queries in the dimension of the space and in the level of approximation. Our results demonstrate that nearoptimal evasion can be accomplished for this family without reverse engineering the classifier's decision boundary. We also consider general lp costs and show that near-optimal evasion on the family of convex-inducing classifiers is generally efficient for both positive and negative convexity for all levels of approximation if p = 1.