research-article

SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL)

Authors:

Rubeena KhaliqAuthors Info & Claims

CAAI Transactions on Intelligence Technology, Volume 6, Issue 3

Pages 281 - 290

https://doi.org/10.1049/cit2.12003

Published: 03 March 2021 Publication History

Abstract

The participation of ordinary devices in networking has created a world of connected devices rapidly. The Internet of Things (IoT) includes heterogeneous devices from every field. There are no definite protocols or standards for IoT communication, and most of the IoT devices have limited resources. Enabling a complete security measure for such devices is a challenging task, yet necessary. Many lightweight security solutions have surfaced lately for IoT. The lightweight security protocols are unable to provide an optimum protection against prevailing powerful threats in cyber world. It is also hard to deploy any traditional security protocol on resource‐constrained IoT devices. Software‐defined networking introduces a centralized control in computer networks. SDN has a programmable approach towards networking that decouples control and data planes. An SDN‐based intrusion detection system is proposed which uses deep learning classifier for detection of anomalies in IoT. The proposed intrusion detection system does not burden the IoT devices with security profiles. The proposed work is executed on the simulated environment. The results of the simulation test are evaluated using various matrices and compared with other relevant methods.

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Cited By

Choudhary VTanwar SChoudhury T(2024)Evaluation of contemporary intrusion detection systems for internet of things environmentMultimedia Tools and Applications10.1007/s11042-023-15918-583:3(7541-7581)Online publication date: 1-Jan-2024
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cover image CAAI Transactions on Intelligence Technology

CAAI Transactions on Intelligence Technology Volume 6, Issue 3

September 2021

125 pages

EISSN:2468-2322

DOI:10.1049/cit2.v6.3

Issue’s Table of Contents

© 2021 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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John Wiley & Sons, Inc.

United States

Publication History

Published: 03 March 2021

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Cited By

Choudhary VTanwar SChoudhury T(2024)Evaluation of contemporary intrusion detection systems for internet of things environmentMultimedia Tools and Applications10.1007/s11042-023-15918-583:3(7541-7581)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15918-5
Dewan RAhmad SRana ANagpal TKumar VSingh NKumar VKumar A(2024)IoT‐based energy efficient and longer lifetime compression approach for healthcare applicationsTransactions on Emerging Telecommunications Technologies10.1002/ett.484335:4Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1002/ett.4843
Ding JLiu BWang JQiao PZhu Z(2023)Digitalization of the Business Environment and Innovation Efficiency of Chinese ICT FirmsJournal of Organizational and End User Computing10.4018/JOEUC.32736535:3(1-25)Online publication date: 14-Aug-2023
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Alzubi OAlzubi JAlzubi TSingh A(2023)Quantum Mayfly Optimization with Encoder-Decoder Driven LSTM Networks for Malware Detection and Classification ModelMobile Networks and Applications10.1007/s11036-023-02105-x28:2(795-807)Online publication date: 7-Feb-2023
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Liatifis ADalamagkas CRadoglou-Grammatikis PLagkas TMarkakis EMladenov VSarigiannidis P(2022)Fault-Tolerant SDN Solution for Cybersecurity ApplicationsProceedings of the 17th International Conference on Availability, Reliability and Security10.1145/3538969.3544479(1-6)Online publication date: 23-Aug-2022
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