Hidden Bibliographic Details
| Other authors / contributors: | Aminanto, Muhamad Erza, author.
Tanuwidjaja, Harry Chan, author.
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| ISBN: | 9789811314445
9811314446
9789811314452
9811314454
9789811314438
9811314438
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| Digital file characteristics: | text file
PDF
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| Notes: | Includes bibliographical references and index.
Online resource; title from PDF file page (EBSCO, viewed October 1, 2018).
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| 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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| Other form: | Printed edition: 9789811314438
Printed edition: 9789811314452
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| Standard no.: | 10.1007/978-981-13-1444-5
10.1007/978-981-13-1
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