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Book synopsis

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.

Book details

Book category Books in English Technology, engineering, agriculture Electronics & communications engineering Communications engineering / telecommunications

• Full title: Network Intrusion Detection using Deep Learning
• Subtitle: A Feature Learning Approach
• Author: Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja
• Language: English
• Binding: Paperback
• Number of pages: 79
• EAN: 9789811314438
• ISBN: 9811314438
• ID: 19690539
• Publisher: Springer Verlag, Singapore
• Weight: 170 g
• Dimensions: 156 × 234 × 11 mm
• Date of publishing: 02. October 2018

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