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Network Intrusion Detection Using Deep Learning: A Feature Learning Approach
Kwangjo Kim
(Author)
·
Muhamad Erza Aminanto
(Author)
·
Harry Chandra Tanuwidjaja
(Author)
·
Springer
· Paperback
Network Intrusion Detection Using Deep Learning: A Feature Learning Approach - Kim, Kwangjo ; Aminanto, Muhamad Erza ; Tanuwidjaja, Harry Chandra
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Synopsis "Network Intrusion Detection Using Deep Learning: A Feature Learning Approach"
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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All books in our catalog are Original.
The book is written in English.
The binding of this edition is Paperback.
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