Randomized Operating Point Selection in Adversarial Classification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00223897" target="_blank" >RIV/68407700:21230/14:00223897 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007/978-3-662-44851-9_16" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-662-44851-9_16</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-662-44851-9_16" target="_blank" >10.1007/978-3-662-44851-9_16</a>

Result language
angličtina
Original language name
Randomized Operating Point Selection in Adversarial Classification
Original language description
Security systems for email spam filtering, network intrusion detection, steganalysis, and watermarking, frequently use classifiers to separate malicious behavior from legitimate. Typically, they use a fixed operating point minimizing the expected cost /error. This allows a rational attacker to deliver invisible attacks just below the detection threshold. We model this situation as a non-zero sum normal form game capturing attacker?s expected payoffs for detected and undetected attacks, and detector?s costs for false positives and false negatives computed based on the Receiver Operating Characteristic (ROC) curve of the classifier. The analysis of Nash and Stackelberg equilibria reveals that using a randomized strategy over multiple operating points forces the rational attacker to design less efficient attacks and substantially lowers the expected cost of the detector. We present the equilibrium strategies for sample ROC curves from network intrusion detection system and evaluate the c
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Machine Learning and Knowledge Discovery in Databases - ECML PKDD 2013, part II
ISBN
978-3-662-44850-2
ISSN
0302-9743
e-ISSN
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Number of pages
16
Pages from-to
240-255
Publisher name
Springer
Place of publication
Heidelberg
Event location
Nancy
Event date
Sep 15, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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