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Network Intrusion Detection using Deep Learning: A Feature Learning Approach

By (author) Kwangjo Kim, By (author) Muhamad Erza Aminanto, By (author) Harry Chandra Tanuwidjaja

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normal price: R 2 058.95

Price: R 1 852.95


| book description |

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.

| product details |



Normally shipped | Available from overseas. Delivery time is 14 - 21 days
Publisher | Springer Verlag, Singapore
Published date | 2 Oct 2018
Language |
Format | Paperback / softback
Pages | 79
Dimensions | 235 x 155 x 0mm (L x W x H)
Weight | 0g
ISBN | 978-9-8113-1443-8
Readership Age |
BISAC | computers / security


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Normally shipped | Available from overseas. Usually dispatched in 14 days
Readership Age |
Normal Price | R 2 741.95
Price | R 2 467.95 | on special |



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