A vulnerability detection method for IoT protocol based on parallel fuzzy algorithm

The Internet of Things communication protocol is prone to security vulnerabilities when facing increasing types and scales of network attacks, which can affect the communication security of the Internet of Things. It is crucial to effectively detect these vulnerabilities in order to improve the secu...

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Bibliographic Details
Published in:Heliyon Vol. 10; no. 12; p. e31846
Main Authors: Han, Yinfeng, Wang, Peng, Kang, Chaoqun, Lin, Jiayin, Fan, Wei
Format: Journal Article
Language:English
Published: England Elsevier Ltd 30.06.2024
Elsevier
Subjects:
algorithms
Communication protocol
exhibitions
Genetic algorithm
genetic improvement
Improved parallelization fuzzy testing algorithm
internet of things
Power distribution IoT
Test case
Vulnerability detection
Communication protocol
Power distribution IoT
Genetic algorithm
Test case
Improved parallelization fuzzy testing algorithm
Vulnerability detection
ISSN:2405-8440, 2405-8440
Online Access:Get full text
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Summary:The Internet of Things communication protocol is prone to security vulnerabilities when facing increasing types and scales of network attacks, which can affect the communication security of the Internet of Things. It is crucial to effectively detect these vulnerabilities in order to improve the security of IoT communication protocols and promptly fix them. Therefore, this study proposes a distributed IoT communication protocol vulnerability detection method based on an improved parallelized fuzzy testing algorithm. Firstly, based on design principles and by comparing different communication protocols, a communication architecture for the distribution network's Internet of Things was constructed, and the communication protocols were formalized and decomposed. Next, preprocess the vulnerability detection samples, and then use genetic algorithm to improve the parallelized fuzzy testing algorithm to perform vulnerability detection. Through this improved algorithm, the missed detection rate and false detection rate can be effectively reduced, thereby improving the security of IoT communication protocols. The experimental results show that the highest missed detection rate of this method is only 4.0 %, and the false detection rate is low, with high detection efficiency. This indicates that the method has good performance and reliability in detecting vulnerabilities in IoT communication protocols.
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ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2024.e31846