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An Effective Detection and Classification Approach for DoS Attacks in Wireless Sensor Networks Using Deep Transfer Learning Models and Majority Voting by Safa Ben Atitallah; Maha Driss; Wadii Boulila; Iman Almomani is a book available to read on EtoBox.

What is An Effective Detection and Classification Approach for DoS Attacks in Wireless Sensor Networks Using Deep Transfer Learning Models and Majority Voting about?

The Internet of Things (IoT) has been used in various critical fields, including healthcare, elderly surveillance, autonomous transportation, and energy management. As a result of its emergence, several IoT-based smart applications have been established in various domains. Wireless Sensor Networks (WSNs) are the most common infrastructure for these applications. A WSN is a network that includes many diverse sensor nodes. These nodes are scattered across large areas, collecting data and transmitting it wirelessly. These networks are subjected to a variety of security threats. Protecting WSNs against incoming threats is both necessary and challenging. This paper presents a deep transfer learningbased approach for intrusion detection and classification in WSNs. To identify and categorize Denial-of-Service (DoS) attacks, we deployed several pre-trained Convolutional Neural Networks (CNNs). To improve the classification performance, the final outputs of the CNN models are combined using ensemble learning, precisely the majority voting method. We used the recent and rich WSN-DS dataset for the experiments, which includes four types of DoS attacks as well as benign samples. The experiment

Author

Safa Ben Atitallah; Maha Driss; Wadii Boulila; Iman Almomani

Publisher

Springer International Publishing : Imprint: Springer

Published

2022

Language

EN

ISBN

9783031162107

Subjects

Education, Computer Science, Business