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books
| book details |
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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| on special |
normal price: R 2 101.95
Price: R 1 996.95
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| 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. Â 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 |
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
| other options |
Normally shipped |
Readership Age |
Normal Price | R 2 591.95
Price | R 2 461.95
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