research-article

Open access

Machine Learning-Based Jamming Detection and Classification in Wireless Networks

Authors:

Luca Arcangeloni,

Andrea GiorgettiAuthors Info & Claims

WiseML'23: Proceedings of the 2023 ACM Workshop on Wireless Security and Machine Learning

Pages 39 - 44

https://doi.org/10.1145/3586209.3591395

Published: 28 June 2023 Publication History

Abstract

The development of novel tools to detect, classify and counteract the new generation of smart jammers in Internet of Things (IoT) is of paramount importance. Detection and classification have to be performed in a short time, with high reliability, and preserving the privacy of network users. In this work, we propose a novel machine learning (ML)-based jamming detection and classification algorithm which can be implemented in the network gateway (GW). The proposed method is based on energy detector (ED), the extraction of specific problem-tailored features, dimensionality reduction, and multi-class classification. Extensive numerical results have been carried out to evaluate the performance of detection and classification, varying the number of principal components selected through dimensionality reduction, the observation window length, the shadowing intensity, and the signal-to-jammer ratio (SJR). Our solution reaches remarkably high accuracy, i.e., up to 99%, outperforming a state-of-the-art solution. That is a very promising result considering that the approach does not need to inspect the decoded information, thus preserving the privacy of the network users.

References

[1]

Ahmad Yusri Dak, Noor Elaiza Abdul Khalid, and Saadiah Yahya. 2012. A novel framework for jamming detection and classification in wireless networks. In International Conference on Computing and Networking Technology (INC, ICCIS and ICMIC) (Gyeongju, Korea (South)). 240--246.

[2]

Zhutian Feng and Cunqing Hua. 2018. Machine Learning-based RF Jamming Detection in Wireless Networks. In International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC) (Shanghai, China). 1--6. https://doi.org/10.1109/SSIC.2018.8556709

[3]

Domenico Giustiniano, Vincent Lenders, Jens B. Schmitt, Michael Spuhler, and Matthias Wilhelm. 2013. Detection of Reactive Jamming in DSSS-Based Wireless Networks. In ACM Conference on Security and Privacy in Wireless and Mobile Networks (Budapest, Hungary). New York, NY, USA, 43--48.

[4]

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. The MIT Press.

Digital Library

[5]

Kanika Grover, Alvin Lim, and Qing Yang. 2014. Jamming and anti-jamming techniques in wireless networks: A survey. International Journal of Ad Hoc and Ubiquitous Computing 17, 4 (Dec. 2014), 197--215.

Digital Library

[6]

Kasturi G.S, Jain Ansh, and Singh Jagdeep. 2020. Detection and Classification of Radio Frequency Jamming Attacks using Machine learning. In Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, Vol. 11. 49--62.

[7]

Zhuyin Li, Andrea Giorgetti, and Sithamparanathan Kandeepan. 2021. Multiple Radio Transmitter Localization via UAV-Based Mapping. IEEE Trans. Veh. Technol. 70, 9 (July 2021), 8811--8822. https://doi.org/10.1109/TVT.2021.3093449

[8]

LoRa Alliance. 2020. RP002--1.0.2 LoRaWAN® Regional Parameters. https://loraalliance. org/resource_hub/rp2--102-lorawan-regional-parameters/

[9]

Andrea Mariani, Andrea Giorgetti, and Marco Chiani. 2011. Effects of Noise Power Estimation on Energy Detection for Cognitive Radio Applications. IEEE Trans. Commun. 59, 12 (Dec. 2011), 3410--3420. https://doi.org/10.1109/TCOMM. 2011.102011.100708

[10]

Andrea Mariani, Andrea Giorgetti, and Marco Chiani. 2012. Test of Independence for Cooperative Spectrum Sensing with Uncalibrated Receivers. In Global Communications Conference (GLOBECOM). Anaheim, CA, USA, 1--6.

[11]

Pawani Porambage, Gürkan Gür, Diana Pamela Moya Osorio, Madhusanka Liyanage, Andrei Gurtov, and Mika Ylianttila. 2021. The Roadmap to 6G Security and Privacy. IEEE Open Journal of the Communications Society 2 (May 2021), 1094-- 1122. https://doi.org/10.1109/OJCOMS.2021.3078081

[12]

Oscar Puñal, Ismet Akta, Caj-Julian Schnelke, Gloria Abidin, Klaus Wehrle, and James Gross. 2014. Machine learning-based jamming detection for IEEE 802.11: Design and experimental evaluation. In IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (Sydney, NSW, Australia). 1--10. https://doi.org/10.1109/WoWMoM.2014.6918964

[13]

Peter J. Smith, Rajitha Senanayake, Pawel A. Dmochowski, Khawla A. Alnajjar, and Andrea Giorgetti. 2022. The Impact of Channel Type on Spectrum Sensing. IEEE Wireless Commun. Lett. 11, 2 (Feb. 2022), 230--234. https://doi.org/10.1109/ LWC.2021.3124506

[14]

Gordon L. Stuber. 2001. Principles of Mobile Communication (second ed.). Kluwer Academic Publishers, Norwell, MA, USA.

[15]

E. Testi and A. Giorgetti. 2021. Blind Wireless Network Topology Inference. IEEE Trans. Commun. 69, 2 (Feb. 2021), 1109--1120. https://doi.org/10.1109/TCOMM. 2020.3036058

[16]

Enrico Testi and Andrea Giorgetti. 2022. RSS-based Localization of Multiple Radio Transmitters via Blind Source Separation. IEEE Commun. Lett. 26, 3 (Mar. 2022), 532--536. https://doi.org/10.1109/LCOMM.2021.3137598

[17]

Muhammad Asad Ullah, Konstantin Mikhaylov, and Hirley Alves. 2022. Analysis and Simulation of LoRaWAN LR-FHSS for Direct-to-Satellite Scenario. IEEE Wireless Commun. Lett. 11, 3 (Mar. 2022), 548--552.

[18]

H. Urkowitz. 1967. Energy detection of unknown deterministic signals. Proc. IEEE 55, 4 (April 1967), 523--531.

[19]

P. Welch. 1967. The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15, 2 (June 1967), 70--73.

[20]

Wenyuan Xu, Wade Trappe, Yanyong Zhang, and Timothy Wood. 2005. The Feasibility of Launching and Detecting Jamming Attacks in Wireless Networks. In ACM International Symposium on Mobile Ad Hoc Networking and Computing (Urbana-Champaign, IL, USA). Association for Computing Machinery, New York, NY, USA, 46--57.

Cited By

Arcangeloni LTesti EGiorgetti A(2024)Cooperative Wideband Spectrum Sensing: a Variational Bayesian Inference Approach2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)10.1109/PIMRC59610.2024.10817413(1-6)Online publication date: 2-Sep-2024
https://doi.org/10.1109/PIMRC59610.2024.10817413

Index Terms

Machine Learning-Based Jamming Detection and Classification in Wireless Networks
1. Networks
  1. Network properties
    1. Network security
      1. Denial-of-service attacks
      2. Mobile and wireless security

Recommendations

Machine learning-based jamming attack classification and effective defense technique
Highlights
- Novel jammer classification and effective defense (JCED) algorithm is proposed.
- Jammer types are effectively classified and suitable countermeasures are initiated.
- JCED achieves Higher effective throughput and lower energy ...
Abstract
The fourth industrial revolution has resulted in the intelligent Internet of Things being widely used for home networking applications and smart infrastructure. Consequently, wireless connectivity has become essential in industrial and daily-life ...
Jamming detection mechanisms for wireless sensor networks
InfoScale '08: Proceedings of the 3rd international conference on Scalable information systems

The Jamming-style Denial of Service (J-DoS) attacks are significant causes of malfunctioning of Wireless Sensor Networks (WSNs). The nodes of WSNs are prone to external disturbances especially when they are used in hostile environments. The attackers ...
Machine learning-driven intrusion detection for Contiki-NG-based IoT networks exposed to NSL-KDD dataset
WiseML '20: Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning

Wide adoption of Internet of Things (IoT) devices and applications encounters security vulnerabilities as roadblocks. The heterogeneous nature of IoT systems prevents common benchmarks, such as the NSL-KDD dataset, from being used to test and verify the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WiseML'23: Proceedings of the 2023 ACM Workshop on Wireless Security and Machine Learning

June 2023

62 pages

ISBN:9798400701337

DOI:10.1145/3586209

Program Chairs:
Deniz Gunduz
Imperial College London, UK
,
Mohammad Malekzadeh
Nokia Bell Labs Cambridge, UK
,
Melek Önen
EURECOM, France
,
Yalin Sagduyu
Virginia Tech, USA
,
Yi Shi
Virginia Tech, USA
,
Junqing Zhang
University of Liverpool, UK

Copyright © 2023 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].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WiSec '23

Sponsor:

WiSec '23: 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks

June 1, 2023

Guildford, United Kingdom

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
452
Total Downloads

Downloads (Last 12 months)301
Downloads (Last 6 weeks)34

Reflects downloads up to 16 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Arcangeloni LTesti EGiorgetti A(2024)Cooperative Wideband Spectrum Sensing: a Variational Bayesian Inference Approach2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)10.1109/PIMRC59610.2024.10817413(1-6)Online publication date: 2-Sep-2024
https://doi.org/10.1109/PIMRC59610.2024.10817413

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents