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Machine Learning-based Jamming Attack Classification and Effective Defense Technique by Sun-Jin Lee; Yu-Rim Lee; So-Eun Jeon; Il-Gu Lee is a Computer Science article available to read on EtoBox.

What is Machine Learning-based Jamming Attack Classification and Effective Defense Technique about?

The fourth industrial revolution has resulted in the intelligent Internet of Things being widely used for home networking applications and smart infrastructure. Consequently, wireless connectivity has become essential in industrial and daily-life applications. Wireless communication is a continuously evolving technology that satisfies high-speed and ultra-low latency requirements. However, as multiple users utilize a single channel by sharing frequency and time, the service quality cannot be ensured owing to the interference from a congested network. Additionally, malicious attackers can compromise communication availability or destroy data integrity through jamming attacks, threatening human life and safety. Conventional jamming attack detection and response technology respond to attacks without detecting the type of jammer, exhibiting certain limitations in detecting and defending against an intelligent attack. This study proposes a novel jammer classification and effective defense (JCED) algorithm that can classify jamming attack types using machine learning (ML) and provide differential responses based on the jamming types. Depending on the jammer type, the JCED algorithm can a

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Author

Sun-Jin Lee; Yu-Rim Lee; So-Eun Jeon; Il-Gu Lee

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)