Jamming Attack Classification in Wireless Networks Using Machine Learning

R. Arathy; Bidisha Bhabani; Madhuri Malakar; Judhistir Mahapatro; Pushpendu Kar

doi:10.1007/978-981-99-2322-9_35

Jamming Attack Classification in Wireless Networks Using Machine Learning

R. Arathy, Bidisha Bhabani, Madhuri Malakar, Judhistir Mahapatro, Pushpendu Kar

School of Computer Science

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

In the field of wireless networks, jamming exhibits severe security threat by creating deliberate network interference. Detection of presence of jammers as well as distinguishing them is important to allow the network to implement combat strategies more efficiently. A unique defence approach needs to be used for each type of jammers making the classification of jammers even more crucial. In this regards, we have used various network parameters to detect and classify them into the constant, random, and reactive categories. We have used machine learning techniques such as decision tree classifier and random forest classifier, and deep learning techniques such as longest short-term memory and multi-layer perceptron for the classification. Accuracy of multi-layer perceptron being 87% is the maximum among all techniques used.

Original language	English
Title of host publication	Communication and Intelligent Systems - Proceedings of ICCIS 2022
Editors	Harish Sharma, Vivek Shrivastava, Kusum Kumari Bharti, Lipo Wang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	477-491
Number of pages	15
ISBN (Print)	9789819923212
DOIs	https://doi.org/10.1007/978-981-99-2322-9_35
Publication status	Published - 2023
Event	4th International Conference on Communication and Intelligent Systems, ICCIS 2022 - New Delhi, India Duration: 19 Dec 2022 → 20 Dec 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	689 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	4th International Conference on Communication and Intelligent Systems, ICCIS 2022
Country/Territory	India
City	New Delhi
Period	19/12/22 → 20/12/22

Keywords

Deep learning
Jamming attacks
Jamming classification
Machine learning

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-99-2322-9_35

Cite this

Arathy, R., Bhabani, B., Malakar, M., Mahapatro, J., & Kar, P. (2023). Jamming Attack Classification in Wireless Networks Using Machine Learning. In H. Sharma, V. Shrivastava, K. K. Bharti, & L. Wang (Eds.), Communication and Intelligent Systems - Proceedings of ICCIS 2022 (pp. 477-491). (Lecture Notes in Networks and Systems; Vol. 689 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-2322-9_35

Arathy, R. ; Bhabani, Bidisha ; Malakar, Madhuri et al. / Jamming Attack Classification in Wireless Networks Using Machine Learning. Communication and Intelligent Systems - Proceedings of ICCIS 2022. editor / Harish Sharma ; Vivek Shrivastava ; Kusum Kumari Bharti ; Lipo Wang. Springer Science and Business Media Deutschland GmbH, 2023. pp. 477-491 (Lecture Notes in Networks and Systems).

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title = "Jamming Attack Classification in Wireless Networks Using Machine Learning",

abstract = "In the field of wireless networks, jamming exhibits severe security threat by creating deliberate network interference. Detection of presence of jammers as well as distinguishing them is important to allow the network to implement combat strategies more efficiently. A unique defence approach needs to be used for each type of jammers making the classification of jammers even more crucial. In this regards, we have used various network parameters to detect and classify them into the constant, random, and reactive categories. We have used machine learning techniques such as decision tree classifier and random forest classifier, and deep learning techniques such as longest short-term memory and multi-layer perceptron for the classification. Accuracy of multi-layer perceptron being 87% is the maximum among all techniques used.",

keywords = "Deep learning, Jamming attacks, Jamming classification, Machine learning",

author = "R. Arathy and Bidisha Bhabani and Madhuri Malakar and Judhistir Mahapatro and Pushpendu Kar",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 4th International Conference on Communication and Intelligent Systems, ICCIS 2022 ; Conference date: 19-12-2022 Through 20-12-2022",

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Arathy, R, Bhabani, B, Malakar, M, Mahapatro, J & Kar, P 2023, Jamming Attack Classification in Wireless Networks Using Machine Learning. in H Sharma, V Shrivastava, KK Bharti & L Wang (eds), Communication and Intelligent Systems - Proceedings of ICCIS 2022. Lecture Notes in Networks and Systems, vol. 689 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 477-491, 4th International Conference on Communication and Intelligent Systems, ICCIS 2022, New Delhi, India, 19/12/22. https://doi.org/10.1007/978-981-99-2322-9_35

Jamming Attack Classification in Wireless Networks Using Machine Learning. / Arathy, R.; Bhabani, Bidisha; Malakar, Madhuri et al.
Communication and Intelligent Systems - Proceedings of ICCIS 2022. ed. / Harish Sharma; Vivek Shrivastava; Kusum Kumari Bharti; Lipo Wang. Springer Science and Business Media Deutschland GmbH, 2023. p. 477-491 (Lecture Notes in Networks and Systems; Vol. 689 LNNS).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Jamming Attack Classification in Wireless Networks Using Machine Learning

AU - Arathy, R.

AU - Bhabani, Bidisha

AU - Malakar, Madhuri

AU - Mahapatro, Judhistir

AU - Kar, Pushpendu

PY - 2023

Y1 - 2023

N2 - In the field of wireless networks, jamming exhibits severe security threat by creating deliberate network interference. Detection of presence of jammers as well as distinguishing them is important to allow the network to implement combat strategies more efficiently. A unique defence approach needs to be used for each type of jammers making the classification of jammers even more crucial. In this regards, we have used various network parameters to detect and classify them into the constant, random, and reactive categories. We have used machine learning techniques such as decision tree classifier and random forest classifier, and deep learning techniques such as longest short-term memory and multi-layer perceptron for the classification. Accuracy of multi-layer perceptron being 87% is the maximum among all techniques used.

AB - In the field of wireless networks, jamming exhibits severe security threat by creating deliberate network interference. Detection of presence of jammers as well as distinguishing them is important to allow the network to implement combat strategies more efficiently. A unique defence approach needs to be used for each type of jammers making the classification of jammers even more crucial. In this regards, we have used various network parameters to detect and classify them into the constant, random, and reactive categories. We have used machine learning techniques such as decision tree classifier and random forest classifier, and deep learning techniques such as longest short-term memory and multi-layer perceptron for the classification. Accuracy of multi-layer perceptron being 87% is the maximum among all techniques used.

KW - Deep learning

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Arathy R, Bhabani B, Malakar M, Mahapatro J, Kar P. Jamming Attack Classification in Wireless Networks Using Machine Learning. In Sharma H, Shrivastava V, Bharti KK, Wang L, editors, Communication and Intelligent Systems - Proceedings of ICCIS 2022. Springer Science and Business Media Deutschland GmbH. 2023. p. 477-491. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-99-2322-9_35

Jamming Attack Classification in Wireless Networks Using Machine Learning

Abstract

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Keywords

ASJC Scopus subject areas

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