Network Intrusion Detection using Deep Learning (häftad)
Format
E-bok
Filformat
EPUB med LCP-kryptering (0.0 MB)
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Nedladdning
Kan laddas ned under 24 månader, dock max 6 gånger.
Språk
Engelska
Utgivningsdatum
2018-09-25
Förlag
Springer Nature Singapore
ISBN
9789811314445

Network Intrusion Detection using Deep Learning E-bok

A Feature Learning Approach

E-bok (LCP),  Engelska, 2018-09-25
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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 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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