Robust Representation for Domain Adaptation in Network Security
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00239342" target="_blank" >RIV/68407700:21230/15:00239342 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-23461-8_8" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-23461-8_8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-23461-8_8" target="_blank" >10.1007/978-3-319-23461-8_8</a>
Alternative languages
Result language
angličtina
Original language name
Robust Representation for Domain Adaptation in Network Security
Original language description
The goal of domain adaptation is to solve the problem of different joint distribution of observation and labels in the training and testing data sets. This problem happens in many practical situations such as when a malware detector is trained from labeled datasets at certain time point but later evolves to evade detection. We solve the problem by introducing a new representation which ensures that a conditional distribution of the observation given labels is the same. The representation is computed forbags of samples (network traffic logs) and is designed to be invariant under shifting and scaling of the feature values extracted from the logs and under permutation and size changes of the bags. The invariance of the representation is achieved by relying on a self-similarity matrix computed for each bag. In our experiments, we will show that the representation is effective for training detector of malicious traffic in large corporate networks. Compared to the case without domain adapta
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Machine Learning and Knowledge Discovery in Databases, Part III
ISBN
978-3-319-23460-1
ISSN
0302-9743
e-ISSN
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Number of pages
17
Pages from-to
116-132
Publisher name
Springer
Place of publication
Heidelberg
Event location
Porto
Event date
Sep 7, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000363667400011