Network Intrusion Detection using Deep Learning / SpringerBriefs on Cyber Security Systems and Networks (PDF)
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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.
Muhamad Erza Aminanto received B.S. and M.S. degrees in Electrical Engineering from Bandung Institute of Technology (ITB), Indonesia in 2013 and 2014, respectively. He is pursuing his Ph.D in the School of Computing at Korea Advanced Institute of Science and Technology (KAIST), South Korea. His current research interests include machine-learning, intrusion detection systems and big data analytics. His recent work entitled "Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection" was published with Kwangjo Kim in IEEE Transactions of Information Forensics and Security (IF:4.332) in 2017.
Harry Chandra Tanuwidajaja received B.S. and M.S. degrees in Electrical Engineering from the Bandung Institute of Technology
- Autoren: Kwangjo Kim , Muhamad Erza Aminanto , Harry Chandra Tanuwidjaja
- 2018, 1st ed. 2018, 79 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 9811314446
- ISBN-13: 9789811314445
- Erscheinungsdatum: 25.09.2018
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- Dateiformat: PDF
- Größe: 2.03 MB
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