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Machine Learning-Based Intrusion Detection System for Smart City

  • Jung Hyun Ryu
  • Jong Hyuk ParkEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 536)

Abstract

Nowadays, the cloud computing-based Internet of Things (IoT) environment suffers from problems such as rapidly increasing data traffic volume, heterogeneity and latency. One of the typical methods to solve these problems is to utilize Fog or Edge Computing, which distributes storage and computing power concentrated in a cloud computing environment through a distributed model. However, in order to compensate for the disadvantages of this distributed network, Mist Computing has emerged as the network model closest to Internet of Things. But, there are thousands of zero-day attacks in the Internet environment of things that communicate by various protocols. Most of these attacks are small variants of previously known attacks. To effectively prevent such attacks, intrusion detection systems in the environment should be more intelligent. In this paper, in order to solve these problems, we propose an artificial intelligence-based intrusion detection system to effectively protect new or continuously changing attacks to IoT in a mist computing environment.

Keywords

Artificial intelligence Intrusion detection IoT Mist computing 

Notes

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No 2016R1A2B4011069).

References

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    Abeshu, A., Chilamkurti, N.: Deep learning: the frontier for distributed attack detection in fog-to-things computing. IEEE Commun. Mag. 56(2), 169–175 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer EngineeringSeoul National University of Science and Technology (SeoulTech)SeoulKorea

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