research-article

Differentiating Attacks and Faults in Energy Aware Smart Home System using Supervised Machine Learning

Authors:

Georgios Tertytchny,

Nicolas Nicolaou,

Maria K. MichaelAuthors Info & Claims

COINS '19: Proceedings of the International Conference on Omni-Layer Intelligent Systems

Pages 122 - 127

https://doi.org/10.1145/3312614.3312642

Published: 05 May 2019 Publication History

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Abstract

The topics of fault diagnosis and security attack diagnosis in Cyber Physical Systems (CPS) have been studied extensively in a stand-alone manner. However, considering the co-existence of both of these sources of abnormality in a system, and being able to distinguish among them, is an important and timely problem not currently addressed in the literature. In this paper, we study the internal communication environment of an Energy Aware Smart Home (EASH) system. We formally define the problem of differentiating component attacks from component failures in EASH and we provide a methodology based on supervised Machine Learning (ML) algorithms to differentiate between a set of common attacks and faults on the communication channel. To evaluate our approach, we provide experimental results obtained by a simulation framework as well as from a real-time testbed environment.

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Cited By

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Sharma DShandilya S(2023)Attack Detection Based on Machine Learning Techniques to Safe and Secure for CPS—A ReviewInternational Conference on IoT, Intelligent Computing and Security10.1007/978-981-19-8136-4_23(273-286)Online publication date: 2-Apr-2023
https://doi.org/10.1007/978-981-19-8136-4_23
Tertytchny GMichael M(2020)Dataset Reduction Framework For Intelligent Fault Detection In IoT-based Cyber-Physical Systems Using Machine Learning Techniques2020 International Conference on Omni-layer Intelligent Systems (COINS)10.1109/COINS49042.2020.9191393(1-6)Online publication date: Aug-2020
https://doi.org/10.1109/COINS49042.2020.9191393

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Differentiating Attacks and Faults in Energy Aware Smart Home System using Supervised Machine Learning

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Published In

COINS '19: Proceedings of the International Conference on Omni-Layer Intelligent Systems

May 2019

241 pages

ISBN:9781450366403

DOI:10.1145/3312614

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 ACM 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 May 2019

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European Union and Republic of Cyprus

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COINS '19

COINS '19: INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS

May 5 - 7, 2019

Crete, Greece

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Sharma DShandilya S(2023)Attack Detection Based on Machine Learning Techniques to Safe and Secure for CPS—A ReviewInternational Conference on IoT, Intelligent Computing and Security10.1007/978-981-19-8136-4_23(273-286)Online publication date: 2-Apr-2023
https://doi.org/10.1007/978-981-19-8136-4_23
Tertytchny GMichael M(2020)Dataset Reduction Framework For Intelligent Fault Detection In IoT-based Cyber-Physical Systems Using Machine Learning Techniques2020 International Conference on Omni-layer Intelligent Systems (COINS)10.1109/COINS49042.2020.9191393(1-6)Online publication date: Aug-2020
https://doi.org/10.1109/COINS49042.2020.9191393

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Cited By

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Recommendations

Classifying network abnormalities into faults and attacks in IoT-based cyber physical systems using machine learning

Machine Learning–based Cyber Attacks Targeting on Controlled Information: A Survey

Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain