| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0455622 |
| utime |
20240103211805.1 |
| mtime |
20160215235959.9 |
| title
(primary) (eng) |
Finding New Malicious Domains Using Variational Bayes on Large-Scale Computer Network Data |
| specification |
| page_count |
10 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0455621 |
| title
|
NIPS Workshop: Advances in Approximate Bayesian Inference |
| page_num |
1-10 |
| publisher |
| place |
Montréal, Canada |
| name |
NIPS |
| year |
2015 |
|
|
| keyword |
variational bayes |
| keyword |
malicious domain detection |
| keyword |
large scale network |
| author
(primary) |
| ARLID |
cav_un_auth*0108231 |
| name1 |
Létal |
| name2 |
V. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0307300 |
| name1 |
Pevný |
| name2 |
T. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0101207 |
| name1 |
Šmídl |
| name2 |
Václav |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101197 |
| name1 |
Somol |
| name2 |
Petr |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept |
Department of Pattern Recognition |
| department (cz) |
RO |
| department |
RO |
| institution |
UTIA-B |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA15-08916S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0328225 |
|
| abstract
(eng) |
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, before 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 observed connections to other related domains. The parameters of the model is inferred by a Variational Bayes method, and its effectiveness is demonstrated on a large-scale network data with millions of domains and trillions of connections to them. |
| action |
| ARLID |
cav_un_auth*0327126 |
| name |
NIPS workshop: Advances in Approximate Bayesian Inference |
| place |
Montreal |
| dates |
11.12.2015 |
| country |
CA |
|
| reportyear |
2016 |
| RIV |
BD |
| num_of_auth |
4 |
| presentation_type |
PO |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0257094 |
| mrcbC61 |
1 |
| confidential |
S |
| arlyear |
2015 |
| mrcbU63 |
cav_un_epca*0455621 NIPS Workshop: Advances in Approximate Bayesian Inference 1 10 Montréal, Canada NIPS 2015 |
|