DoS Attack Detection in Wireless Sensor Networks (WSN) Using Hybrid Machine Learning Model

Wireless sensor networks (WSNs) are utilized in a variety of applications where distant data gathering is required, such as environmental monitoring, military applications, traffic control and health monitoring among others. They are made up of numerous inexpensive, compact sensor nodes, which colla...

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Bibliographic Details
Published in:	2023 10th International Conference on Signal Processing and Integrated Networks (SPIN) pp. 384 - 388
Main Authors:	Rao, G. Sankara, Harshitha, Muvvala, Joshitha, Vuriti Rama, Sravya, Saripaka Srilakshmi, Priya, Mutcha Vishnu
Format:	Conference Proceeding
Language:	English
Published:	IEEE 23-03-2023
Subjects:	Denial-of-service attack DoS attack Hybrid Models Machine Learning Network Security Security Signal processing Signal processing algorithms Support vector machines Traffic control Wireless sensor networks Wireless Sensor Networks (WSN)
Online Access:	Get full text
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Summary:	Wireless sensor networks (WSNs) are utilized in a variety of applications where distant data gathering is required, such as environmental monitoring, military applications, traffic control and health monitoring among others. They are made up of numerous inexpensive, compact sensor nodes, which collaborate to gather data and transmit it to the Base Station (BS) for analysis.WSNs use radio frequencies to communicate and due to this, WSNs are vulnerable to various attacks, including Denial of Service (DoS) attacks. A Denial-of-Service attack is any operation that disables the network from carrying out the planned tasks and transmits unnecessary packets while attempting to use network bandwidth. The network user is unable to access the services when they are demanded. Algorithms like Support Vector Machine, k-Nearest Neighbour, Naïve Bayes, and Random Forest are used to construct a Hybrid Machine Learning model. This approach will be used to identify potential targets for such attacks before they occur.
ISSN:	2688-769X
DOI:	10.1109/SPIN57001.2023.10117098