Learning Invariant Representation for Malicious Network Traffic Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00309233" target="_blank" >RIV/68407700:21230/16:00309233 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3233/978-1-61499-672-9-1132" target="_blank" >http://dx.doi.org/10.3233/978-1-61499-672-9-1132</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/978-1-61499-672-9-1132" target="_blank" >10.3233/978-1-61499-672-9-1132</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning Invariant Representation for Malicious Network Traffic Detection

Popis výsledku v původním jazyce

Statistical learning theory relies on an assumption that the joint distributions of observations and labels are the same in training and testing data. However, this assumption is violated in many real world problems, such as training a detector of malicious network traffic that can change over time as a result of attacker's detection evasion efforts. We propose to address this problem by creating an optimized representation, which significantly increases the robustness of detectors or classifiers trained under this distributional shift. The representation is created from bags of samples (e.g. 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 is achieved by combining feature histograms with feature self-similarity matrices computed for each bag and significantly reduces the difference between the training and testing data. The parameters of the representation, such as histogram bin boundaries, are learned jointly with the classifier. We show that the representation is effective for training a detector of malicious traffic, achieving 90% precision and 67% recall on samples of previously unseen malware variants.

Název v anglickém jazyce

Learning Invariant Representation for Malicious Network Traffic Detection

Popis výsledku anglicky

Statistical learning theory relies on an assumption that the joint distributions of observations and labels are the same in training and testing data. However, this assumption is violated in many real world problems, such as training a detector of malicious network traffic that can change over time as a result of attacker's detection evasion efforts. We propose to address this problem by creating an optimized representation, which significantly increases the robustness of detectors or classifiers trained under this distributional shift. The representation is created from bags of samples (e.g. 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 is achieved by combining feature histograms with feature self-similarity matrices computed for each bag and significantly reduces the difference between the training and testing data. The parameters of the representation, such as histogram bin boundaries, are learned jointly with the classifier. We show that the representation is effective for training a detector of malicious traffic, achieving 90% precision and 67% recall on samples of previously unseen malware variants.

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

European Conference on Artificial Intelligence

ISBN

978-1-61499-671-2

ISSN

0922-6389

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1132-1139

Název nakladatele

IOS Press

Místo vydání

Amsterdam

Místo konání akce

Hague

Datum konání akce

29. 8. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000385793700132