Network Intrusion Detection using Deep Learning - A Feature Learning Approach (Paperback, 1st ed. 2018)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	SpringerBriefs on Cyber Security Systems and Networks
Release date:	September 2018
First published:	2018
Authors:	Kwangjo Kim • Muhamad Erza Aminanto • Harry Chandra Tanuwidjaja
Dimensions:	235 x 155 x 11mm (L x W x T)
Format:	Paperback
Pages:	79
Edition:	1st ed. 2018
ISBN-13:	978-981-13-1443-8
Categories:	Books > Computing & IT > Computer communications & networking > Network security Books > Professional & Technical > Electronics & communications engineering > Communications engineering / telecommunications > WAP (wireless) technology Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	981-13-1443-8
Barcode:	9789811314438

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