| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0463379 |
| utime |
20240103212704.3 |
| mtime |
20161004235959.9 |
| SCOPUS |
85001945953 |
| WOS |
000391051600008 |
| DOI |
10.1145/2996758.2996761 |
| title
(primary) (eng) |
Discriminative Models for Multi-instance Problems with Tree Structure |
| specification |
| page_count |
9 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0464075 |
| ISBN |
978-1-4503-4573-6 |
| title
|
Proceedings of the 9th ACM Workshop on Artificial Intelligence and Security 2016 |
| publisher |
| place |
New York |
| name |
ACM |
| year |
2016 |
|
|
| keyword |
Neural netwrok |
| keyword |
User modeling |
| keyword |
Malware detection |
| keyword |
Big data |
| keyword |
Learning indicators of compromise |
| author
(primary) |
| ARLID |
cav_un_auth*0101197 |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept (eng) |
Department of Pattern Recognition |
| department (cz) |
RO |
| department (eng) |
RO |
| full_dept |
Department of Pattern Recognition |
| name1 |
Somol |
| name2 |
Petr |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0307300 |
| name1 |
Pevný |
| name2 |
T. |
| country |
CZ |
|
| source |
|
| cas_special |
| abstract
(eng) |
Modelling network traffic is gaining importance to counter modern security threats of ever increasing sophistication. It is though surprisingly difficult and costly to construct reliable classifiers on top of telemetry data due to the variety and complexity of signals that no human can manage to interpret in full. Obtaining training data with sufficiently large and variable body of labels can thus be seen as a prohibitive problem. The goal of this work is to detect infected computers by observing their HTTP(S) traffic collected from network sensors, which are typically proxy servers or network firewalls, while relying on only minimal human input in the model training phase. We propose a discriminative model that makes decisions based on a computer’s all traf.c observed during a predefined time window (5 minutes in our case). |
| action |
| ARLID |
cav_un_auth*0334128 |
| name |
9th ACM Workshop on Artificial Intelligence and Security |
| dates |
28.10.2016 |
| place |
Vienna |
| country |
AT |
|
| RIV |
IN |
| reportyear |
2017 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0263127 |
| mrcbC61 |
1 |
| cooperation |
| ARLID |
cav_un_auth*0334129 |
| name |
Cisco R&D center in Prague |
| institution |
CISCO |
| country |
CZ |
|
| confidential |
S |
| mrcbC86 |
n.a. Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods |
| arlyear |
2016 |
| mrcbU14 |
85001945953 SCOPUS |
| mrcbU34 |
000391051600008 WOS |
| mrcbU63 |
cav_un_epca*0464075 Proceedings of the 9th ACM Workshop on Artificial Intelligence and Security 2016 978-1-4503-4573-6 New York ACM 2016 |
|