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
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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
KW - Jamming attacks
KW - Jamming classification
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85168994906&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-2322-9_35
DO - 10.1007/978-981-99-2322-9_35
M3 - Conference contribution
AN - SCOPUS:85168994906
SN - 9789819923212
T3 - Lecture Notes in Networks and Systems
SP - 477
EP - 491
BT - Communication and Intelligent Systems - Proceedings of ICCIS 2022
A2 - Sharma, Harish
A2 - Shrivastava, Vivek
A2 - Bharti, Kusum Kumari
A2 - Wang, Lipo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Communication and Intelligent Systems, ICCIS 2022
Y2 - 19 December 2022 through 20 December 2022
ER -