A framework for quantitative security analysis of machine learning

Proceedings of the ACM Conference on Computer and Communications Security

Volumn , Issue , 2009, Pages 1-4

  Laskov, Pavel ^a,c   Kloft, Marius ^b  

^a UNIVERSITY OF TÜBINGEN   (Germany)

^b TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^c FRAUNHOFER FOKUS   (Germany)

Author keywords

Adversarial learning; Centroid anomaly detection; Computer security; Intrusion detection; Machine learning

Indexed keywords

ADVERSARIAL LEARNING; ANOMALY DETECTION; COMPUTER SECURITY; FORMAL SPECIFICATION; KEY PARTS; LEARNING MODELS; MACHINE LEARNING METHODS; MACHINE-LEARNING; SECURITY ANALYSIS; SPECIFIC LEARNING; THEORETICAL BOUNDS; UPPER BOUND;

ARTIFICIAL INTELLIGENCE; COMPUTER CRIME; LEARNING SYSTEMS; NETWORK SECURITY; SECURITY SYSTEMS;

INTRUSION DETECTION;

EID: 74049158178     PISSN: 15437221     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1654988.1654990     Document Type: Conference Paper

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