skip to main content
10.1145/3386723.3387892acmotherconferencesArticle/Chapter ViewAbstractPublication PagesnissConference Proceedingsconference-collections
research-article

Performance Assessment of Open Source IDS for improving IoT Architecture Security implemented on WBANs

Published: 18 May 2020 Publication History

Abstract

The evolution of the IoT field and its strong presence in multiple domains has raised new security challenges and concerns, which explain the large number of existing studies on this phenomenon. Despite the fact that many security solutions have been applied, security threats and vulnerabilities remain present with new leaks discovery and malicious attempt to gain unauthorized access. In this research, we tackle this problem by providing a new solution of implementing open source IDS (Intrusion Detection Systems) into an IoT architecture and we perform a comprehensive study of the performance of our selected IDS regarding their detection rate and usage consumption.
The proposed model is innovative since it brings a novel approach of implementing existing IDSs over the WBANs network taking into consideration all specifications and characteristics of the different layers that compose the architecture of the IoT system implemented to monitor human health.

References

[1]
Khaleel Ahmad, Shikha Verma, Nitesh Kumar and Jayant Shekhar "Classification of internet security attacks", Proceedings of the 5th National Conference; INDIACom-2011 Computing For Nation Development, New Delhi, p. 1, March 2011.
[2]
Alhomoud, Adeeb & Munir, Rashid & Pagna Diss, Jules & Awan, Irfan & Al-Dhelaan, Abdullah. (2011). Performance Evaluation Study of Intrusion Detection Systems.Procedia CS. 5. December 2011. 10.1016/j.procs.2011.07.024.
[3]
P. Mehra, "A brief study and comparison of snort and bro open source network intrusion detection systems," International Journal of Advanced Research in Computer and Communication Engineering Vol. 1, Issue 6, August 2012, ISSN: 2278 -- 1021.
[4]
White, Joshua & Fitsimmons, Thomas & Matthews, Jeanna. (2013). Quantitative Analysis of Intrusion Detection Systems: Snort and Suricata. Proceedings of SPIE - The International Society for Optical Engineering. April 2013. 10.1117/12.2015616.
[5]
Sforzin, Alessandro & Gomez Marmol, Felix & Conti, Mauro & Bohli, Jens-Matthias. (2016). RPiDS: Raspberry Pi IDS --- A Fruitful Intrusion Detection System for IoT. 440--448. July 2016.
[6]
Sheikh, Nazim Uddin & Rahman, Hasina & Vikram, Shashwat & Alqahtani, Hamed. (2018). A Lightweight Signature-Based IDS for IoT Environment. November 2018.
[7]
Nam, Kiho & Kim, Keecheon. (2018). A Study on SDN security enhancement using open source IDS/IPS Suricata. October 2018. 10.1109/ICTC.2018.8539455.
[8]
Bouziani, Ossama & Benaboud, Hafssa & Chamkar, Achraf & Lazaar, Saiida. (2019). A Comparative study of Open Source IDSs according to their Ability to Detect Attacks. March 2019.
[9]
Isa, Fuad & Saad, Shahadan & Fadzil, Ahmad & Md Saidi, Raihana. (2019). Comprehensive Performance Assessment on Open Source Intrusion Detection System. October 2019.
[10]
Hallaj Asghar, Mohsen & Negi, Atul & m.zadeh, Nasibeh. (2015). Principle application and vision in Internet of Things (IoT). International Conference on Computing, Communication and Automation, ICCCA 2015. 427--431. 10.1109/CCAA.2015.7148413.
[11]
Moloja, Dina. (2018), Cloud Intrusion Detection and Prevention System for M-Voting Application in South Africa: Suricata vs. Snort. 10.1007/978-3-319-77028-4_18.

Cited By

View all
  • (2024)Tecnologías y herramientas del internet de las cosas (IoT) para el desarrollo de prototipos de entornos cotidianosREVISTA COLOMBIANA DE TECNOLOGIAS DE AVANZADA (RCTA)10.24054/rcta.v2i44.30202:44(97-103)Online publication date: 24-Jul-2024
  • (2023)Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2023.327022511(41796-41815)Online publication date: 2023
  • (2023)Intrusion Detection Systems in Internet of Things Using Machine Learning Algorithms: A Comparative StudyInternational Conference on Advanced Intelligent Systems for Sustainable Development10.1007/978-3-031-35251-5_12(132-144)Online publication date: 9-Jun-2023

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
NISS '20: Proceedings of the 3rd International Conference on Networking, Information Systems & Security
March 2020
528 pages
ISBN:9781450376341
DOI:10.1145/3386723
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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2020

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Bro
  2. Intrusion Detection Systems
  3. IoT
  4. Snort
  5. Suricata
  6. WBAN

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

NISS2020

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)15
  • Downloads (Last 6 weeks)0
Reflects downloads up to 14 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Tecnologías y herramientas del internet de las cosas (IoT) para el desarrollo de prototipos de entornos cotidianosREVISTA COLOMBIANA DE TECNOLOGIAS DE AVANZADA (RCTA)10.24054/rcta.v2i44.30202:44(97-103)Online publication date: 24-Jul-2024
  • (2023)Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2023.327022511(41796-41815)Online publication date: 2023
  • (2023)Intrusion Detection Systems in Internet of Things Using Machine Learning Algorithms: A Comparative StudyInternational Conference on Advanced Intelligent Systems for Sustainable Development10.1007/978-3-031-35251-5_12(132-144)Online publication date: 9-Jun-2023
  • (2022)Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and DatasetsSensors10.3390/s2210374422:10(3744)Online publication date: 14-May-2022

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media