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An Efficient Intrusion Detection Model for Edge System in Brownfield Industrial Internet of Things

Authors:

Muna Al-Hawawreh,

Elena Sitnikova,

Frank den HartogAuthors Info & Claims

BDIOT '19: Proceedings of the 3rd International Conference on Big Data and Internet of Things

Pages 83 - 87

https://doi.org/10.1145/3361758.3361762

Published: 22 August 2019 Publication History

Abstract

The Industrial Internet of Things (IIoT) is bringing control systems online leading to significant innovation in industry and business. However, this development also comes with new cybersecurity threats. As much of the value of IIoT systems resides at the edge tier, this makes them potentially desired targets for attackers. Protecting edge physical systems by monitoring them and identifying malicious activities based on an efficient detection model is therefore of utmost importance. This paper proposes a detection model based on deep learning techniques that can learn and test using data collected from Remote Telemetry Unit (RTU) streams of gas pipeline system. It utilizes the sparse and denoising auto-encoder methods for unsupervised learning and deep neural networks for supervised learning to produce a high-level data representation from unlabeled and noisy data. Our results show that the proposed model achieves superior performance in identifying malicious activities.

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Cited By

Zeng WYang HWang Y(2024)Intrusion Detection Based on Feature Selection and Transformer BiGRUData Science10.1007/978-981-97-8743-2_10(134-144)Online publication date: 31-Oct-2024
https://doi.org/10.1007/978-981-97-8743-2_10
Gungor ORosing TAksanli B(2023)Adversarial-HD: Hyperdimensional Computing Adversarial Attack Design for Secure Industrial Internet of ThingsProceedings of Cyber-Physical Systems and Internet of Things Week 202310.1145/3576914.3587484(1-6)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1145/3576914.3587484
Nuaimi MFourati LHamed B(2023)Intelligent approaches toward intrusion detection systems for Industrial Internet of ThingsJournal of Network and Computer Applications10.1016/j.jnca.2023.103637215:COnline publication date: 24-May-2023
https://dl.acm.org/doi/10.1016/j.jnca.2023.103637
Show More Cited By

Index Terms

An Efficient Intrusion Detection Model for Edge System in Brownfield Industrial Internet of Things
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems

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cover image ACM Other conferences

BDIOT '19: Proceedings of the 3rd International Conference on Big Data and Internet of Things

August 2019

139 pages

ISBN:9781450372466

DOI:10.1145/3361758

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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University of Pisa: University of Pisa
La Trobe University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 August 2019

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BDIOT 2019

BDIOT 2019: The 3rd International Conference on Big Data and Internet of Things

August 22 - 24, 2019

VIC, Melbourn, Australia

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Overall Acceptance Rate 75 of 136 submissions, 55%

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Cited By

Zeng WYang HWang Y(2024)Intrusion Detection Based on Feature Selection and Transformer BiGRUData Science10.1007/978-981-97-8743-2_10(134-144)Online publication date: 31-Oct-2024
https://doi.org/10.1007/978-981-97-8743-2_10
Gungor ORosing TAksanli B(2023)Adversarial-HD: Hyperdimensional Computing Adversarial Attack Design for Secure Industrial Internet of ThingsProceedings of Cyber-Physical Systems and Internet of Things Week 202310.1145/3576914.3587484(1-6)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1145/3576914.3587484
Nuaimi MFourati LHamed B(2023)Intelligent approaches toward intrusion detection systems for Industrial Internet of ThingsJournal of Network and Computer Applications10.1016/j.jnca.2023.103637215:COnline publication date: 24-May-2023
https://dl.acm.org/doi/10.1016/j.jnca.2023.103637
Mohawesh RXu SSpringer MJararweh YAl-Hawawreh MMaqsood S(2023)An explainable ensemble of multi-view deep learning model for fake review detectionJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.10164435:8(101644)Online publication date: Sep-2023
https://doi.org/10.1016/j.jksuci.2023.101644
Al-Hawawreh MAljuhani AJararweh Y(2023)Chatgpt for cybersecurity: practical applications, challenges, and future directionsCluster Computing10.1007/s10586-023-04124-526:6(3421-3436)Online publication date: 24-Aug-2023
https://doi.org/10.1007/s10586-023-04124-5
Naz NKhan MAlsuhibany SDiyan MTan ZKhan MAhmad J(2022)Ensemble learning-based IDS for sensors telemetry data in IoT networksMathematical Biosciences and Engineering10.3934/mbe.202249319:10(10550-10580)Online publication date: 2022
https://doi.org/10.3934/mbe.2022493
Inayat UZia MMahmood SKhalid HBenbouzid M(2022)Learning-Based Methods for Cyber Attacks Detection in IoT Systems: A Survey on Methods, Analysis, and Future ProspectsElectronics10.3390/electronics1109150211:9(1502)Online publication date: 7-May-2022
https://doi.org/10.3390/electronics11091502
Telikani AShen JYang JWang P(2022)Industrial IoT Intrusion Detection via Evolutionary Cost-Sensitive Learning and Fog ComputingIEEE Internet of Things Journal10.1109/JIOT.2022.31882249:22(23260-23271)Online publication date: 15-Nov-2022
https://doi.org/10.1109/JIOT.2022.3188224
Al-Hawawreh MSitnikova EAboutorab N(2022)X-IIoTID: A Connectivity-Agnostic and Device-Agnostic Intrusion Data Set for Industrial Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2021.31020569:5(3962-3977)Online publication date: 1-Mar-2022
https://doi.org/10.1109/JIOT.2021.3102056
Clottey RYaokumah WAppati J(2021)Modelling and Evaluation of Network Intrusion Detection Systems Using Machine Learning TechniquesInternational Journal of Intelligent Information Technologies10.4018/IJIIT.28997117:4(81-99)Online publication date: 1-Oct-2021
https://doi.org/10.4018/IJIIT.289971
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