Summary and Further Challenges

  • Kwangjo Kim
  • Muhamad Erza Aminanto
  • Harry Chandra Tanuwidjaja
Part of the SpringerBriefs on Cyber Security Systems and Networks book series (BRIEFSCSSN)


This last chapter concludes this monograph by providing a closing statement regarding the advantage of using deep learning models for IDS purposes and why those models can improve IDS performance. Afterward, the overview of challenges and future research directions in deep learning applications for IDS is suggested.


Deep Learning Models Good Anomaly Detection Controller Area Network (CAN) Image Recognition Field Intrusion Prevention System (IPS) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd., part of Springer Nature 2018

Authors and Affiliations

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

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