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Books > Computing & IT > Computer communications & networking > Network security
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Network Intrusion Detection using Deep Learning - A Feature Learning Approach (Paperback, 1st ed. 2018)
Loot Price: R1,767
Discovery Miles 17 670
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Network Intrusion Detection using Deep Learning - A Feature Learning Approach (Paperback, 1st ed. 2018)
Series: SpringerBriefs on Cyber Security Systems and Networks
Expected to ship within 10 - 15 working days
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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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