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<title><![CDATA[Comentarios al libro: NETWORK INTRUSION DETECTION USING DEEP LEARNING]]></title>
<link><![CDATA[https://www.biblioeteca.com/biblioeteca.web/titulo/network-intrusion-detection-using-deep-learning]]></link>
<description><![CDATA[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.<p>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.]]></description>
<lastBuildDate>Mon, 31 Aug 2026 13:30:29 +0000</lastBuildDate>
<language>es</language>
<copyright>Copyright 202 6BiblioEteca Technologies SL</copyright>

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