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 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.