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Network Security Monitoring and Anal...

Li, Bingdong.

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Network Security Monitoring and Analysis based on Big Data Technologies.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Network Security Monitoring and Analysis based on Big Data Technologies./
作者:	Li, Bingdong.
面頁冊數:	127 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.
Contained By:	Dissertation Abstracts International75-05B(E).
標題:	Engineering, Computer. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3608749
ISBN:	9781303671524

Network Security Monitoring and Analysis based on Big Data Technologies.
Li, Bingdong.

Network Security Monitoring and Analysis based on Big Data Technologies. - 127 p.

Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.

Thesis (Ph.D.)--University of Nevada, Reno, 2013.

This item is not available from ProQuest Dissertations & Theses.

Network flow data provides valuable information to understand the network status and to be aware of the network security threads. However, handling the large amount of data collected from the network and providing real time information remain as big challenges. Big Data technologies provide new approaches to collect, store, real time measurement and analysis of the large amount of data. This dissertation aims to provide a system of network security monitoring and analysis based on the Big Data technologies.

ISBN: 9781303671524Subjects--Topical Terms:

1669061
Engineering, Computer.

Network Security Monitoring and Analysis based on Big Data Technologies.
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245 1 0 $a Network Security Monitoring and Analysis based on Big Data Technologies.
300 $a 127 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.
500 $a Advisers: Mehmet H. Gunes; George Bebis.
502 $a Thesis (Ph.D.)--University of Nevada, Reno, 2013.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Network flow data provides valuable information to understand the network status and to be aware of the network security threads. However, handling the large amount of data collected from the network and providing real time information remain as big challenges. Big Data technologies provide new approaches to collect, store, real time measurement and analysis of the large amount of data. This dissertation aims to provide a system of network security monitoring and analysis based on the Big Data technologies.
520 $a First, I present an extensive survey of the network flow applications that covers past research perspectives, methodologies, and a discussion of challenges and future works. Then, I present system design of the network security monitoring and analysis platform based on the Big Data technologies. Components of this system include Flume and Kafka for real time distributed data collection, Storm for real time streaming distributed data processing, Cassandra for NoSQL data storage, data processing, and user interfaces. The system supports real time continuous network monitoring, interactive visualization, network measurement, and advanced network modeling to classify host roles based on host behaviors and to identify a particular user among the other users.
520 $a It is critical to continuously monitor the network status and network security threats in real time, but it is a challenge to process these large amount of data in real time. I demonstrate how the Big Data security system designed in this dissertation supports such features. For instance, querying a network host 24 hours network traffic took 56 milliseconds round-trip. Another usage of the network flow data is to measure the contents and usage of the network. I demonstrate how this Big Data system provides understanding of the usage of anonymity technologies on the campus Internet. Then I present methods and the results of classification and identification of network objects based on the Big Data system designed in this dissertation. Decision Tree and On-Line Support Vector Machine are used to model host role behaviors and user behaviors. I report very high accuracy of host role classification and user identification.
590 $a School code: 0139.
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710 2 $a University of Nevada, Reno. $b Computer Engineering. $3 1058048
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792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3608749