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Network Intrusion Detection using Deep Learning [electronic resource] : A Feature Learning Approach / by Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja.

By: Kim, Kwangjo [author.].
Contributor(s): Aminanto, Muhamad Erza [author.] | Tanuwidjaja, Harry Chandra [author.] | SpringerLink (Online service).
Series: SpringerBriefs on Cyber Security Systems and Networks: Publisher: Singapore : Springer Singapore : Imprint: Springer, 2018Description: XVII, 79 p. 30 illus., 11 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789811314445.Subject(s): Data protection | Artificial intelligence | Wireless communication systems | Mobile communication systems | Big data | Data mining | SecurityAdditional physical formats: Printed edition:: No title; Printed edition:: No title
Contents:
Chapter 1 Introduction -- Chapter 2 Intrusion Detection Systems -- Chapter 3 Classical Machine Learning and Its Applications to IDS -- Chapter 4 Deep Learning -- Chapter 5 Deep Learning-based IDSs -- Chapter 6 Deep Feature Learning -- Chapter 7 Summary and Further Challenges.
Summary: 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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Chapter 1 Introduction -- Chapter 2 Intrusion Detection Systems -- Chapter 3 Classical Machine Learning and Its Applications to IDS -- Chapter 4 Deep Learning -- Chapter 5 Deep Learning-based IDSs -- Chapter 6 Deep Feature Learning -- Chapter 7 Summary and Further Challenges.

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.

Online version restricted to NUS staff and students only through NUSNET.

Mode of access: World Wide Web.

System requirements: Internet connectivity; World Wide Web browser.

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