Network Intrusion Detection using Deep Learning: A Feature Learning Approach
Network Intrusion Detection using Deep Learning: A Feature Learning Approach (SpringerBriefs on Cyber Security Systems and Networks) by Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja
English | October 2, 2018 | ISBN: 9811314438 | 96 pages | MOBI | 3.88 Mb
English | October 2, 2018 | ISBN: 9811314438 | 96 pages | MOBI | 3.88 Mb
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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