Network Intrusion Detection using Deep Learning:A Feature Learning Approach (SpringerBriefs on Cyber Security Systems and Networks)
Authors:Kwangjo Kim - Muhamad Erza Aminanto - Harry Chandra Tanuwidjaja
Edition 版次：1st ed. 2018
Release Finelybook 出版日期：2018-09-26
pages 页数：79 pages
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