research-article

Evaluating Machine Learning Methods for Intrusion Detection in IoT

Authors:

Shini GirijaAuthors Info & Claims

ICICM '22: Proceedings of the 12th International Conference on Information Communication and Management

Pages 7 - 12

https://doi.org/10.1145/3551690.3551692

Published: 14 September 2022 Publication History

Abstract

Cyber-attacks in IoT enabled devices have grown at an alarming rate since the start of the Covid-19 pandemic due to cyber physical digital transformation enabled through widespread deployment of low cost sensor embedded IoT devices in consumer and industrial IOT, as well as increase in computing power. Consequently, this adoption trend had led to 1.51 billion breaches on IoT devices during the first half of 2021 alone. This highlights the critical importance of being prepared for IoT vulnerabilities (IoT manufacturing and deployment sector) and attacks (malicious actors). In this respect machine learning (ML) especially deep learning (DL) strategies has emerged as the preferred methods to secure IoT devices from attacks. In this paper, we propose three deep learning algorithms for IoT intrusion detection based on mapping of IoT attacks to ML/DL methods. Our paper thus provides two contributions. First, we present a model that maps extant research on the application of ML/DL to specific IoT attacks. Second, through an optimal selection of the mapping, we present three algorithms (naïve Bayes, convolutional neural network and autoencoder) for detection of intrusion in IoT attacks. This provides a review of research opportunities and research gaps in the IoT IDS domain.

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Cited By

Kumar GKatiyar ASrinivasan K(2024)Elevating IDS Capabilities: The Convergence of SVM, Deep Learning, and RFECV in Network Security2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)10.1109/ic-ETITE58242.2024.10493239(1-16)Online publication date: 22-Feb-2024
https://doi.org/10.1109/ic-ETITE58242.2024.10493239
Fikriansyah MAmatullah Karimah SSetiadi F(2024)Detection of DDOS Attacks in IIoT Case Using Machine Learning Algorithms2024 International Conference on Data Science and Its Applications (ICoDSA)10.1109/ICoDSA62899.2024.10652225(117-121)Online publication date: 10-Jul-2024
https://doi.org/10.1109/ICoDSA62899.2024.10652225

Index Terms

Evaluating Machine Learning Methods for Intrusion Detection in IoT
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems

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ICICM '22: Proceedings of the 12th International Conference on Information Communication and Management

July 2022

105 pages

ISBN:9781450396493

DOI:10.1145/3551690

Copyright © 2022 ACM.

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Publication History

Published: 14 September 2022

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ICICM 2022

ICICM 2022: 2022 The 12th International Conference on Information Communication and Management

July 13 - 15, 2022

London, United Kingdom

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Cited By

Kumar GKatiyar ASrinivasan K(2024)Elevating IDS Capabilities: The Convergence of SVM, Deep Learning, and RFECV in Network Security2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)10.1109/ic-ETITE58242.2024.10493239(1-16)Online publication date: 22-Feb-2024
https://doi.org/10.1109/ic-ETITE58242.2024.10493239
Fikriansyah MAmatullah Karimah SSetiadi F(2024)Detection of DDOS Attacks in IIoT Case Using Machine Learning Algorithms2024 International Conference on Data Science and Its Applications (ICoDSA)10.1109/ICoDSA62899.2024.10652225(117-121)Online publication date: 10-Jul-2024
https://doi.org/10.1109/ICoDSA62899.2024.10652225

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