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Statements

Subject Item
n2:RIV%2F63839172%3A_____%2F14%3A10130411%21RIV15-MSM-63839172
rdf:type
skos:Concept n20:Vysledek
rdfs:seeAlso
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7033254
dcterms:description
The Domain Name System (DNS) belongs to crucial services in a computer network. Because of its importance, DNS is usually allowed in security policies. That opens a way to break policies and to transfer data from/to restricted area due to misusage of a DNS infrastructure. This paper is focused on a detection of communication tunnels and other anomalies in a DNS traffic. The proposed detection module is designed to process huge volume of data and to detect anomalies at near real-time. It is based on combination of statistical analysis of several observed features including application layer information. Our aim is a stream-wise processing of huge volume of DNS data from backbone networks. To achieve these objectives with minimal resource consumption, the detection module uses efficient extended data structures. The performance evaluation has shown that the detector is able to process approximately 511 thousand DNS flow records per second. In addition, according to experiments, a tunnel that lasts over 30 seconds can be detected in a minute. During the on-line testing on a real traffic from production network, the module signalized on average over 60 confirmed alerts including DNS tunnels per day. The Domain Name System (DNS) belongs to crucial services in a computer network. Because of its importance, DNS is usually allowed in security policies. That opens a way to break policies and to transfer data from/to restricted area due to misusage of a DNS infrastructure. This paper is focused on a detection of communication tunnels and other anomalies in a DNS traffic. The proposed detection module is designed to process huge volume of data and to detect anomalies at near real-time. It is based on combination of statistical analysis of several observed features including application layer information. Our aim is a stream-wise processing of huge volume of DNS data from backbone networks. To achieve these objectives with minimal resource consumption, the detection module uses efficient extended data structures. The performance evaluation has shown that the detector is able to process approximately 511 thousand DNS flow records per second. In addition, according to experiments, a tunnel that lasts over 30 seconds can be detected in a minute. During the on-line testing on a real traffic from production network, the module signalized on average over 60 confirmed alerts including DNS tunnels per day.
dcterms:title
Stream-wise Detection of Surreptitious Traffic over DNS Stream-wise Detection of Surreptitious Traffic over DNS
skos:prefLabel
Stream-wise Detection of Surreptitious Traffic over DNS Stream-wise Detection of Surreptitious Traffic over DNS
skos:notation
RIV/63839172:_____/14:10130411!RIV15-MSM-63839172
n4:aktivita
n17:P
n4:aktivity
P(LM2010005)
n4:dodaniDat
n10:2015
n4:domaciTvurceVysledku
n7:7697406
n4:druhVysledku
n16:D
n4:duvernostUdaju
n21:S
n4:entitaPredkladatele
n8:predkladatel
n4:idSjednocenehoVysledku
47681
n4:idVysledku
RIV/63839172:_____/14:10130411
n4:jazykVysledku
n12:eng
n4:klicovaSlova
iodine; communication tunnels; DNS; anomaly detection; stream-wise detection
n4:klicoveSlovo
n5:communication%20tunnels n5:stream-wise%20detection n5:DNS n5:iodine n5:anomaly%20detection
n4:kontrolniKodProRIV
[9E7EC11CFD13]
n4:mistoKonaniAkce
Athens
n4:mistoVydani
Pomona, California, USA
n4:nazevZdroje
2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) (CAMAD 2014)
n4:obor
n14:IN
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
3
n4:projekt
n13:LM2010005
n4:rokUplatneniVysledku
n10:2014
n4:tvurceVysledku
Kubátová, Hana Rosa, Zdeněk Čejka, Tomáš
n4:typAkce
n22:WRD
n4:zahajeniAkce
2014-12-01+01:00
s:numberOfPages
5
n11:doi
10.1109/CAMAD.2014.7033254
n18:hasPublisher
IEEE Communications Society
n19:isbn
978-1-4799-5725-5