Denial-of-Service (DoS) Attack Detection within a Network of Wireless Sensors via Adaptive Sunflower Optimization and Improved Deep Convolutional Neural Network

Journal: GRENZE International Journal of Engineering and Technology
Authors: Sheela S K, C M Prajwal, Lavanya Singh M K, Nisarga K M, Nithyashree S Jain
Volume: 10 Issue: 2
Grenze ID: 01.GIJET.10.2.713 Pages: 5878-5883

Abstract

The popularity of wireless sensor networks (WSNs) is rising quickly, and because of their adaptability and simplicity of use, a growing number of security risks arise. For this reason, network intrusion defense research is necessary for WSNs. A cyberattack known as denial-ofservice (DoS) brings down the targeted network. A WSN device will be destroyed by a DoS assault. Because each node functions independently of the others and there isn't a central or monitoring node, it is vulnerable to malicious assaults and challenging to prevent. To guarantee encrypted communication, many techniques have been used in real time. This technique improves the initial settings of IDCNN using the ASFO approach in order to avoid entering the local optimum. Then, intrusions in WSNs are found using the ASFO-IDCNN method. Several simulated scenarios' outcomes are displayed, and the related data is contrasted. Research on DoS protection is useful when examining how effective WSN nodes are at thwarting attacks.

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