research-article

Active Learning for Wireless IoT Intrusion Detection

Authors:

Weiyi ZhangAuthors Info & Claims

IEEE Wireless Communications, Volume 25, Issue 6

Pages 19 - 25

https://doi.org/10.1109/MWC.2017.1800079

Published: 01 December 2018 Publication History

Abstract

The Internet of Things (IoT) is becoming truly ubiquitous in our everyday lives, but it also faces unique security challenges. Intrusion detection is critical for the security and safety of a wireless IoT network. This article discusses the human-in-theloop active learning approach for wireless intrusion detection. We first present the fundamental challenges against the design of a successful intrusion detection system for a wireless IoT network. We then briefly review the rudimentary concepts of active learning and propose its employment in the diverse applications of wireless intrusion detection. An experimental example is also presented to show the significant performance improvement of the active learning method over the traditional supervised learning approach. While machine learning (ML) techniques have been widely employed for intrusion detection, the application of human-in-the-loop ML that leverages both machine and human intelligence to intrusion detection of IoT is still in its infancy. We hope this article can assist readers in understanding the key concepts of active learning and spur further research in this area.

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Cited By

Shen SCai CLi ZShen YWu GYu S(2024)Deep Q-network-based heuristic intrusion detection against edge-based SIoT zero-day attacksApplied Soft Computing10.1016/j.asoc.2023.111080150:COnline publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.asoc.2023.111080
Zhang YDong HNottingham ABuchanan MBrown DSun Y(2023)Global Analysis with Aggregation-based Beaconing Detection across Large Campus NetworksProceedings of the 39th Annual Computer Security Applications Conference10.1145/3627106.3627126(565-579)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3627106.3627126
Deng XZhu JPei XZhang LLing ZXue K(2023)Flow Topology-Based Graph Convolutional Network for Intrusion Detection in Label-Limited IoT NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2022.321380720:1(684-696)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1109/TNSM.2022.3213807
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Index Terms

Active Learning for Wireless IoT Intrusion Detection
1. Computing methodologies
  1. Machine learning

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cover image IEEE Wireless Communications

IEEE Wireless Communications Volume 25, Issue 6

December 2018

126 pages

ISSN:1536-1284

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Copyright © 2018.

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IEEE Press

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Published: 01 December 2018

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Cited By

Shen SCai CLi ZShen YWu GYu S(2024)Deep Q-network-based heuristic intrusion detection against edge-based SIoT zero-day attacksApplied Soft Computing10.1016/j.asoc.2023.111080150:COnline publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.asoc.2023.111080
Zhang YDong HNottingham ABuchanan MBrown DSun Y(2023)Global Analysis with Aggregation-based Beaconing Detection across Large Campus NetworksProceedings of the 39th Annual Computer Security Applications Conference10.1145/3627106.3627126(565-579)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3627106.3627126
Deng XZhu JPei XZhang LLing ZXue K(2023)Flow Topology-Based Graph Convolutional Network for Intrusion Detection in Label-Limited IoT NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2022.321380720:1(684-696)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1109/TNSM.2022.3213807
Shafi MJha RJain S(2023)LGTBIDS: Layer-Wise Graph Theory-Based Intrusion Detection System in Beyond 5GIEEE Transactions on Network and Service Management10.1109/TNSM.2022.319792120:1(658-671)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1109/TNSM.2022.3197921
Qiu CYang KWang JZhao S(2023)AI Empowered Net-RCA for 6GIEEE Network: The Magazine of Global Internetworking10.1109/MNET.130.220035237:6(132-140)Online publication date: 4-Jan-2023
https://dl.acm.org/doi/10.1109/MNET.130.2200352
Olímpio GCamargos LMiani RFaria E(2023)Model update for intrusion detectionComputers and Security10.1016/j.cose.2023.103451134:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.cose.2023.103451
Ma HYang KPun M(2023)Cellular traffic prediction via deep state space models with attention mechanismComputer Communications10.1016/j.comcom.2022.10.023197:C(276-283)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.comcom.2022.10.023
Alzubi OMalik HChaudhary GSrivastava S(2022)A deep learning- based frechet and dirichlet model for intrusion detection in IWSNJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-18975642:2(873-883)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-189756
Sohrabi FJiang TCui WYu W(2022)Active Sensing for Communications by LearningIEEE Journal on Selected Areas in Communications10.1109/JSAC.2022.315549640:6(1780-1794)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1109/JSAC.2022.3155496
Guarascio MCassavia NPisani FManco G(2022)Boosting Cyber-Threat Intelligence via Collaborative Intrusion DetectionFuture Generation Computer Systems10.1016/j.future.2022.04.028135:C(30-43)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.future.2022.04.028
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