Detecting Jamming Attacks in Wireless Communication Using Machine Learning Models

Paradesi Subba Rao, Nooka Varsha Reddy, Shaik Suhani, Kasetty Susmitha, Lalam Jhansi, Pinjari Aneefa

2025

Abstract

Attacks from indiscriminate jammers could prevent communications by disrupting wireless networks. In conventional jamming detection systems, software-defined radios or fixed-threshold signal evaluation algorithms are incorporated in an attempt to solve the problem. These methods do not cope well with sophisticated and adaptive jamming techniques due to inflexibility, excessive resource expenditure, and high rates of false alarms. Fixed-threshold techniques cannot be adjusted adaptively, while methods based on SDR necessitate high volumes of both processing power and costly radio frequency hardware. Machine learning algorithms based jamming detection systems take features such as RSSI, SNR, BER, and packet loss rate as detection model metrics. The proposed solution enhances the robustness and security of wireless communication systems by providing fast, adaptable and hardware-independent response to jamming inquiries.

Paper Citation

in Harvard Style

Rao P., Reddy N., Suhani S., Susmitha K., Jhansi L. and Aneefa P. (2025). Detecting Jamming Attacks in Wireless Communication Using Machine Learning Models. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 63-66. DOI: 10.5220/0013891700004919

in Bibtex Style

@conference{icrdicct`2525,
author={Paradesi Rao and Nooka Reddy and Shaik Suhani and Kasetty Susmitha and Lalam Jhansi and Pinjari Aneefa},
title={Detecting Jamming Attacks in Wireless Communication Using Machine Learning Models},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={63-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013891700004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Detecting Jamming Attacks in Wireless Communication Using Machine Learning Models
SN - 978-989-758-777-1
AU - Rao P.
AU - Reddy N.
AU - Suhani S.
AU - Susmitha K.
AU - Jhansi L.
AU - Aneefa P.
PY - 2025
SP - 63
EP - 66
DO - 10.5220/0013891700004919
PB - SciTePress