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The Effect of K-Nearest Neighbors Classifier for Intrusion Detection of Streaming Net-Flows in Apache Spark Environment.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	The Effect of K-Nearest Neighbors Classifier for Intrusion Detection of Streaming Net-Flows in Apache Spark Environment./
Author:	Thevar, Muthukumar.
Description:	1 online resource (64 pages)
Notes:	Source: Masters Abstracts International, Volume: 56-06.
Subject:	Information technology. -
Online resource:	click for full text (PQDT)
ISBN:	9780355135350

The Effect of K-Nearest Neighbors Classifier for Intrusion Detection of Streaming Net-Flows in Apache Spark Environment.
Thevar, Muthukumar.

The Effect of K-Nearest Neighbors Classifier for Intrusion Detection of Streaming Net-Flows in Apache Spark Environment. - 1 online resource (64 pages)

Source: Masters Abstracts International, Volume: 56-06.

Thesis (M.S.)--University of Maryland, Baltimore County, 2017.

Includes bibliographical references

An Intrusion Detection System (IDS) is built with the purpose to detect normal and attack packets in network traffic data. Due to enormous amount of data present in the network traffic, analyzing all the individual packets present is both an impractical task which also increases the system performance overhead. To solve this problem, another technique is employed, which aggregates packet information into flows and reduces the amount of data to be examined from the network traffic. In addition, IDS efficiency is increased by the use of the k-NN classification algorithm to classify the incoming connections as normal or suspicious. Combining the flow based Intrusion detection approach and k-NN classifier in the Spark Streaming framework has helped develop a system which is able to detect attacks in real time. In this thesis, the KDD-99 data set has been used for testing the proposed approaches. Experimental results show that Apache Spark Streaming, a modern distributed stream processing system provides enough throughput to process large volumes of data in shorter span of time which is suitable for network traffic monitoring.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355135350Subjects--Topical Terms:

559429
Information technology.
Index Terms--Genre/Form:

554714
Electronic books.

The Effect of K-Nearest Neighbors Classifier for Intrusion Detection of Streaming Net-Flows in Apache Spark Environment.
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500 $a Source: Masters Abstracts International, Volume: 56-06.
500 $a Adviser: George Karabatis.
502 $a Thesis (M.S.)--University of Maryland, Baltimore County, 2017.
504 $a Includes bibliographical references
520 $a An Intrusion Detection System (IDS) is built with the purpose to detect normal and attack packets in network traffic data. Due to enormous amount of data present in the network traffic, analyzing all the individual packets present is both an impractical task which also increases the system performance overhead. To solve this problem, another technique is employed, which aggregates packet information into flows and reduces the amount of data to be examined from the network traffic. In addition, IDS efficiency is increased by the use of the k-NN classification algorithm to classify the incoming connections as normal or suspicious. Combining the flow based Intrusion detection approach and k-NN classifier in the Spark Streaming framework has helped develop a system which is able to detect attacks in real time. In this thesis, the KDD-99 data set has been used for testing the proposed approaches. Experimental results show that Apache Spark Streaming, a modern distributed stream processing system provides enough throughput to process large volumes of data in shorter span of time which is suitable for network traffic monitoring.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Information technology. $3 559429
650 4 $a Information science. $3 561178
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0723
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710 2 $a University of Maryland, Baltimore County. $b Information Systems. $3 1179394
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