Finding New Malicious Domains Using Variational Bayes on Large-Scale Computer Network Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00237786" target="_blank" >RIV/68407700:21230/15:00237786 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.approximateinference.org/accepted/LetalEtAl2015.pdf" target="_blank" >http://www.approximateinference.org/accepted/LetalEtAl2015.pdf</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Finding New Malicious Domains Using Variational Bayes on Large-Scale Computer Network Data

Popis výsledku v původním jazyce

The common limitation in computer network security is the reactive nature of defenses. A new type of infection typically needs to be first observed live, be- fore defensive measures can be taken. To improve the pro-active measures, we have developed a method utilizing WHOIS database (database of entities that has registered a particular domain) to model relations between domains even those not yet used. The model estimates the probability of a domain name being used for malicious purposes from observedconnections to other related domains. The parameters of the model is inferred by a Variational Bayes method, and its effec- tiveness is demonstrated on a large-scale network data with millions of domains and trillions of connections to them.

Název v anglickém jazyce

Finding New Malicious Domains Using Variational Bayes on Large-Scale Computer Network Data

Popis výsledku anglicky

The common limitation in computer network security is the reactive nature of defenses. A new type of infection typically needs to be first observed live, be- fore defensive measures can be taken. To improve the pro-active measures, we have developed a method utilizing WHOIS database (database of entities that has registered a particular domain) to model relations between domains even those not yet used. The model estimates the probability of a domain name being used for malicious purposes from observedconnections to other related domains. The parameters of the model is inferred by a Variational Bayes method, and its effec- tiveness is demonstrated on a large-scale network data with millions of domains and trillions of connections to them.

Druh

O - Ostatní výsledky

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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