Network Intrusion Detection using Deep Learning : A Feature Learning Approach

Language: English

Published by Springer, 2018

9811314438 / 9789811314438

Series: Book 6 of 10 - SpringerBriefs on Cyber Security Systems and Networks

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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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Druck auf Anfrage Neuware - Printed after ordering - 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.…

Seller Inventory # 9789811314438

Title
Network Intrusion Detection using Deep Learning : A Feature Learning Approach
Author
Muhamad Erza Aminanto
Publisher
Springer
Publication year
2018
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
9811314438
ISBN 13
9789811314438
Item weight
166 grams
Dimensions
235x155x6 mm
Series
Book 6 of 10: SpringerBriefs on Cyber Security Systems and Networks

AHA-BUCH GmbH

Einbeck, Germany

5-star seller

AbeBooks seller since August 14, 2006

Shipping rates from Germany to U.S.A.

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