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An Intrusion Detection System for Constrained WSN and IoT Nodes Based on Binary Logistic Regression

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MSWIM '18: Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

Pages 259 - 263

https://doi.org/10.1145/3242102.3242145

Published: 25 October 2018 Publication History

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Abstract

In this paper we evaluate the feasibility of running a lightweight Intrusion Detection System within a constrained sensor or IoT node. We propose mIDS, which monitors and detects attacks using a statistical analysis tool based on Binary Logistic Regression (BLR). mIDS takes as input only local node parameters for both benign and malicious behavior and derives a normal behavior model that detects abnormalities within the constrained node.We offer a proof of correct operation by testing mIDS in a setting where network-layer attacks are present. In such a system, critical data from the routing layer is obtained and used as a basis for profiling sensor behavior. Our results show that, despite the lightweight implementation, the proposed solution achieves attack detection accuracy levels within the range of 96% - 100%.

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Cited By

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Raja PKumar AKumar RGoswami B(2024)An Overview of Problems and Difficulties with ML in WSNs ProtectionEuropean Journal of Applied Science, Engineering and Technology10.59324/ejaset.2024.2(2).182:2(245-278)Online publication date: 1-Mar-2024
https://doi.org/10.59324/ejaset.2024.2(2).18
Kumar IBohra MMohd NSingh T(2024)Distributed Denial of Services (DDoS) & IoT Botnet Malware Identification Using Machine Learning & Deep Learning Models2024 Second International Conference on Advances in Information Technology (ICAIT)10.1109/ICAIT61638.2024.10690305(1-6)Online publication date: 24-Jul-2024
https://doi.org/10.1109/ICAIT61638.2024.10690305
Ghadi YMazhar TShloul TShahzad TSalaria UAhmed AHamam H(2024)Machine Learning Solutions for the Security of Wireless Sensor Networks: A ReviewIEEE Access10.1109/ACCESS.2024.335531212(12699-12719)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3355312
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Index Terms

An Intrusion Detection System for Constrained WSN and IoT Nodes Based on Binary Logistic Regression
1. Networks
  1. Network types
    1. Cyber-physical networks
      1. Sensor networks
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems
  2. Network security
    1. Denial-of-service attacks

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Published In

MSWIM '18: Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

October 2018

372 pages

ISBN:9781450359603

DOI:10.1145/3242102

General Chair:
Azzedine Boukerche
University of Ottawa, Canada
,
Program Chairs:
Salil Kanhere
University of New South Wales, Australia
,
Paolo Bellavista
University of Bologna, Italy

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: 25 October 2018

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MSWIM '18

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SIGSIM

MSWIM '18: 21st ACM Int'l Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems

October 28 - November 2, 2018

QC, Montreal, Canada

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Overall Acceptance Rate 398 of 1,577 submissions, 25%

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https://doi.org/10.59324/ejaset.2024.2(2).18
Kumar IBohra MMohd NSingh T(2024)Distributed Denial of Services (DDoS) & IoT Botnet Malware Identification Using Machine Learning & Deep Learning Models2024 Second International Conference on Advances in Information Technology (ICAIT)10.1109/ICAIT61638.2024.10690305(1-6)Online publication date: 24-Jul-2024
https://doi.org/10.1109/ICAIT61638.2024.10690305
Ghadi YMazhar TShloul TShahzad TSalaria UAhmed AHamam H(2024)Machine Learning Solutions for the Security of Wireless Sensor Networks: A ReviewIEEE Access10.1109/ACCESS.2024.335531212(12699-12719)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3355312
El khediri SBenfradj AThaljaoui AMoulahi TUllah Khan RAlabdulatif ALorenz P(2024)Integration of artificial intelligence (AI) with sensor networks: Trends, challenges, and future directionsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.10189236:1(101892)Online publication date: Jan-2024
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