Isbn: 9789811314438 - network intrusion detection using deep learning: a feature learning approach (springerbriefs on cyber security systems and networks) (4 Ergebnisse)

Zustand: New. In English.

Paperback. Zustand: Brand New. 100 pages. 9.25x6.25x0.25 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Taschenbuch. Zustand: Neu. Network Intrusion Detection using Deep Learning | A Feature Learning Approach | Kwangjo Kim (u. a.) | Taschenbuch | SpringerBriefs on Cyber Security Systems and Networks | xvii | Englisch | 2018 | Springer | EAN 9789811314438 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…