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CCS
2009
ACM

A framework for quantitative security analysis of machine learning

12 years 9 days ago
A framework for quantitative security analysis of machine learning
We propose a framework for quantitative security analysis of machine learning methods. Key issus of this framework are a formal specification of the deployed learning model and an attackers constraints, the computation of an optimal attack, and a derivation of an upper bound on the adversarial impact. We exemplarily apply the framework for the analysis of one specific learning scenario, online centroid anomaly detection and experimentally verify the tightness of obtained theoretical bounds. Categories and Subject Descriptors C.2.0 [Computer-Communication Networks]: General—Security and protection; I.2.6 [Artificial Intelligence]: Learning—Parameter learning; I.5.2 [Pattern Recognition]: Design Methodology—Classifier design and evaluation General Terms Algorithms, Experimentation, Security Keywords Machine learning, computer security, centroid anomaly detection, intrusion detection, adversarial learning
Pavel Laskov, Marius Kloft
Added 19 May 2010
Updated 19 May 2010
Type Conference
Year 2009
Where CCS
Authors Pavel Laskov, Marius Kloft
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