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공공누리This item is licensed Korea Open Government License

dc.contributor.author
이종숙
dc.date.accessioned
2019-08-28T07:41:28Z
dc.date.available
2019-08-28T07:41:28Z
dc.date.issued
2014-09-30
dc.identifier.issn
1569-190x
dc.identifier.uri
https://repository.kisti.re.kr/handle/10580/14251
dc.identifier.uri
http://www.ndsl.kr/ndsl/search/detail/article/articleSearchResultDetail.do?cn=NART70735530
dc.description.abstract
Over the last few years, the quantity of teletraffic is rapidly growing because
of the explosive increase of Internet users and its applications. The needs
of collection, storage, management, analysis, and measurement of the subsequent
teletraffic have emerged as some of the very important issues. To this
point many studies for detecting anomaly teletraffic have been done. Detection,
measurement, and analysis studies for traffic data, however, are not
actively being made based on Hadoop. In this paper, some problems and solutions
for those systems have been suggested. We have also designed and developed
an Anomaly Teletraffic detection Measurement analysis Simulator,
called the ATMSim. One strong point of the ATMSim is able to store,
measure, and analyze traffic data for detecting anomaly teletraffic. The
other strength is to generate sequences of input synthetic anomaly teletraffic
with various network attacks for practical network security applications. All
simulations were executed under the control of the ATMSim simulator to investigate
how input anomaly teletraffic with network attacks can be different
from real Ethernet local area network (LAN) traffic. Our numerical results show that the values of the estimated Hurst parameter obtained from the
anomaly teletraffic are much higher when compared to real Ethernet LAN
traffic.
dc.language
eng
dc.relation.ispartofseries
Simulation modelling practice & theory
dc.title
ATMSim: An Anomaly Teletraffic Detection Measurement Analysis Simulator
dc.subject.keyword
Anomaly teletraffic detection
dc.subject.keyword
Simulator
dc.subject.keyword
Stochastic self-similarity
dc.subject.keyword
Hurst parameter
dc.subject.keyword
Hadoop
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7. KISTI 연구성과 > 학술지 발표논문
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