Research on Multi-Source IoT Device Recognition Based on Improved GWO Algorithm
The increasing number of IoT devices has led to great challenges in data capacity storage and data security in IoT.In order to monitor IoT devices in real time and provide better security services.The study first introduces the gray wolf optimization algorithm for combination based on the introduction of support vector ma-chine classifiers,while accesses the cosine value transformation law for parameter optimization.Then a multi-classifier combination is carried out with integrated learning and a novel device recognition model is constructed.The experimental results show that the class accuracy of the improved gray wolf optimization algorithm is up to 95%,the minimum number of iterations is 1180,the precision is up to 93.4%,the recall value is up to 92.8%,and the F1 value is up to 92.9%.7 devices are able to accomplish more than 90 points in the recognition test.It can be seen that the improved gray wolf optimization algorithm and the final recognition model of the in-stitute have high robustness and superiority,and can provide certain technical support for the technical develop-ment in the field of IoT device recognition.
Internet of Thingsdevice identificationgray wolf optimization algorithmsupport vector ma-chinecosine value variation
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