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Semi-Supervised Machine Learning for...

Shi, Ningxin.

Semi-Supervised Machine Learning for Network Intrusion Detection.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Semi-Supervised Machine Learning for Network Intrusion Detection./
作者:	Shi, Ningxin.
面頁冊數:	1 online resource (38 pages)
附註:	Source: Masters Abstracts International, Volume: 57-06.
Contained By:	Masters Abstracts International57-06(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355990614

Semi-Supervised Machine Learning for Network Intrusion Detection.
Shi, Ningxin.

Semi-Supervised Machine Learning for Network Intrusion Detection. - 1 online resource (38 pages)

Source: Masters Abstracts International, Volume: 57-06.

Thesis (M.S.)--North Carolina Agricultural and Technical State University, 2018.

Includes bibliographical references

Various machine learning techniques have been used for network intrusion detection. The supervised machine learning methods can achieve high accuracy in classifying normal and abnormal network data. However, a large amount of labeled data is needed to acquire high accuracy. Labeling large amount of data could be very costly. Semi-supervised machine learning techniques overcome this problem since they only use a small amount of labeled data and large amount of unlabeled data.

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

Mode of access: World Wide Web

ISBN: 9780355990614Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Semi-Supervised Machine Learning for Network Intrusion Detection.
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500 $a Source: Masters Abstracts International, Volume: 57-06.
500 $a Adviser: Xiaohong Yuan.
502 $a Thesis (M.S.)--North Carolina Agricultural and Technical State University, 2018.
504 $a Includes bibliographical references
520 $a Various machine learning techniques have been used for network intrusion detection. The supervised machine learning methods can achieve high accuracy in classifying normal and abnormal network data. However, a large amount of labeled data is needed to acquire high accuracy. Labeling large amount of data could be very costly. Semi-supervised machine learning techniques overcome this problem since they only use a small amount of labeled data and large amount of unlabeled data.
520 $a In this research, semi-supervised Support Vector machine (SVM), Random Forest and Deep Belief Network (DBN) were used in classifying network data for intrusion detection. They were used to classify the Third International Knowledge Discovery and Data Mining Tools Competition dataset (KDD 1999). The results of semi-supervised Random Forest for classifying normal and abnormal network data were compared with the results of using supervised Random Forest. The results were also compared with semi-supervised ladder network in classifying KDD 1999. Self-learning based semi-supervised Support Vector Machine (SVM) and Deep Belief Network (DBN) were also used to classify the specific attack types in KDD 1999.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
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