Network Intrusion Detection using Deep Learning: A Feature Learning Approach (SpringerBriefs on Cyber Security Systems and Networks)
By 作者: Kwangjo Kim – Muhamad Erza Aminanto – Harry Chandra Tanuwidjaja
ISBN-10 书号: 9811314438
ISBN-13 书号: 9789811314438
Edition 版本: 1st ed. 2018
Release Finelybook 出版日期: 2018-09-26
pages 页数: (79 )
Book Description to Finelybook sorting
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.
2.Intrusion Detection Systems
3.Classical Machine Learning and Its Applications to DS
5.Deep Learning-Based IDSs
6.Deep Feature Learning
7.Summary and Further Challenges
Network Intrusion Detection using Deep Learning: A Feature Learning Approach