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Network Intrusion Detection using Deep Learning

A Feature Learning Approach
eBook

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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... > mehr
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Autor: Harry Chandra Tanuwidjaja, Kwangjo Kim, Muhamad Erza Aminanto
Anbieter: Springer
Sprache: Englisch
EAN: 9789811314445
Veröffentlicht: 25.09.2018
Format: PDF
Schutz: DRM Dieses eBook ist durch "Digital Rights Management" kurz DRM geschützt. Das bedeutet, dass Sie nach dem Kauf eines PDF-eBooks eine ACSM-Datei als Download erhalten. Sie benötigen für die Anzeige auf Ihrem Gerät die Software „Adobe Digital Editions“.

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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