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Network intrusion detection using de...

Aminanto, Muhamad Erza.

Network intrusion detection using deep learninga feature learning approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Network intrusion detection using deep learningby Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja.
Reminder of title:	a feature learning approach /
Author:	Kim, Kwangjo.
other author:	Aminanto, Muhamad Erza.
Published:	Singapore :Springer Singapore :2018.
Description:	xvii, 79 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-981-13-1444-5
ISBN:	9789811314445$q(electronic bk.)

Network intrusion detection using deep learninga feature learning approach /
Kim, Kwangjo.

Network intrusion detection using deep learninga feature learning approach /[electronic resource] :by Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja. - Singapore :Springer Singapore :2018. - xvii, 79 p. :ill., digital ;24 cm. - SpringerBriefs on cyber security systems and networks,2522-5561. - SpringerBriefs on cyber security systems and networks..

Chapter 1 Introduction -- Chapter 2 Intrusion Detection Systems -- Chapter 3 Classical Machine Learning and Its Applications to IDS -- Chapter 4 Deep Learning -- Chapter 5 Deep Learning-based IDSs -- Chapter 6 Deep Feature Learning -- Chapter 7 Summary and Further Challenges.

This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different classes: generative, discriminative, and adversarial networks. Moreover, it compares various deep learning-based IDSs based on benchmarking datasets. The book also proposes two novel feature learning models: deep feature extraction and selection (D-FES) and fully unsupervised IDS. Further challenges and research directions are presented at the end of the book. Offering a comprehensive overview of deep learning-based IDS, the book is a valuable reerence resource for undergraduate and graduate students, as well as researchers and practitioners interested in deep learning and intrusion detection. Further, the comparison of various deep-learning applications helps readers gain a basic understanding of machine learning, and inspires applications in IDS and other related areas in cybersecurity.

ISBN: 9789811314445$q(electronic bk.)

Standard No.: 10.1007/978-981-13-1444-5doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Network intrusion detection using deep learninga feature learning approach /
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245 1 0 $a Network intrusion detection using deep learning $h [electronic resource] : $b a feature learning approach / $c by Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja.
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505 0 $a Chapter 1 Introduction -- Chapter 2 Intrusion Detection Systems -- Chapter 3 Classical Machine Learning and Its Applications to IDS -- Chapter 4 Deep Learning -- Chapter 5 Deep Learning-based IDSs -- Chapter 6 Deep Feature Learning -- Chapter 7 Summary and Further Challenges.
520 $a This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different classes: generative, discriminative, and adversarial networks. Moreover, it compares various deep learning-based IDSs based on benchmarking datasets. The book also proposes two novel feature learning models: deep feature extraction and selection (D-FES) and fully unsupervised IDS. Further challenges and research directions are presented at the end of the book. Offering a comprehensive overview of deep learning-based IDS, the book is a valuable reerence resource for undergraduate and graduate students, as well as researchers and practitioners interested in deep learning and intrusion detection. Further, the comparison of various deep-learning applications helps readers gain a basic understanding of machine learning, and inspires applications in IDS and other related areas in cybersecurity.
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