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Machine Learning Based Rank Attack Detection for Smart Hospital Infrastructure
In recent years, many technologies were racing to deliver the best service for human being. Emerging Internet of Things (IoT) technologies made birth to the notion of smart infrastructures such as smart grid, smart factories or smart hospitals. These infrastructures rely on interconnected smart devi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313290/ http://dx.doi.org/10.1007/978-3-030-51517-1_3 |
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author | Said, Abd Mlak Yahyaoui, Aymen Yaakoubi, Faicel Abdellatif, Takoua |
author_facet | Said, Abd Mlak Yahyaoui, Aymen Yaakoubi, Faicel Abdellatif, Takoua |
author_sort | Said, Abd Mlak |
collection | PubMed |
description | In recent years, many technologies were racing to deliver the best service for human being. Emerging Internet of Things (IoT) technologies made birth to the notion of smart infrastructures such as smart grid, smart factories or smart hospitals. These infrastructures rely on interconnected smart devices collecting real-time data in order to improve existing procedures and systems capabilities. A critical issue in smart infrastructures is the information protection which may be more valuable than physical assets. Therefore, it is extremely important to detect and deter any attacks or breath to the network system for information theft. One of these attacks is the rank attack that is carried out by an intruder node in order to attract legitimate traffic to it, then steal personal data of different persons (both patients and staffs in hospitals). In this paper, we propose an anomaly based rank attack detection system against an IoT network using Support Vector Machines. As a use case, we are interested in the healthcare sector and in particular in smart hospitals which are multifaceted with many challenges such as service resilience, assets interoperability and sensitive information protection. The proposed intrusion detection system (IDS) is implemented and evaluated using Conticki Cooja simulator. Results show a high detection accuracy and low false positive rates. |
format | Online Article Text |
id | pubmed-7313290 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73132902020-06-24 Machine Learning Based Rank Attack Detection for Smart Hospital Infrastructure Said, Abd Mlak Yahyaoui, Aymen Yaakoubi, Faicel Abdellatif, Takoua The Impact of Digital Technologies on Public Health in Developed and Developing Countries Article In recent years, many technologies were racing to deliver the best service for human being. Emerging Internet of Things (IoT) technologies made birth to the notion of smart infrastructures such as smart grid, smart factories or smart hospitals. These infrastructures rely on interconnected smart devices collecting real-time data in order to improve existing procedures and systems capabilities. A critical issue in smart infrastructures is the information protection which may be more valuable than physical assets. Therefore, it is extremely important to detect and deter any attacks or breath to the network system for information theft. One of these attacks is the rank attack that is carried out by an intruder node in order to attract legitimate traffic to it, then steal personal data of different persons (both patients and staffs in hospitals). In this paper, we propose an anomaly based rank attack detection system against an IoT network using Support Vector Machines. As a use case, we are interested in the healthcare sector and in particular in smart hospitals which are multifaceted with many challenges such as service resilience, assets interoperability and sensitive information protection. The proposed intrusion detection system (IDS) is implemented and evaluated using Conticki Cooja simulator. Results show a high detection accuracy and low false positive rates. 2020-05-31 /pmc/articles/PMC7313290/ http://dx.doi.org/10.1007/978-3-030-51517-1_3 Text en © The Author(s) 2020 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
spellingShingle | Article Said, Abd Mlak Yahyaoui, Aymen Yaakoubi, Faicel Abdellatif, Takoua Machine Learning Based Rank Attack Detection for Smart Hospital Infrastructure |
title | Machine Learning Based Rank Attack Detection for Smart Hospital Infrastructure |
title_full | Machine Learning Based Rank Attack Detection for Smart Hospital Infrastructure |
title_fullStr | Machine Learning Based Rank Attack Detection for Smart Hospital Infrastructure |
title_full_unstemmed | Machine Learning Based Rank Attack Detection for Smart Hospital Infrastructure |
title_short | Machine Learning Based Rank Attack Detection for Smart Hospital Infrastructure |
title_sort | machine learning based rank attack detection for smart hospital infrastructure |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313290/ http://dx.doi.org/10.1007/978-3-030-51517-1_3 |
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