SD-WSN Network Security Detection Methods for Online Network Education
Abstract
Online network education faces serious information security problems, and traditional network security detection methods are insufficient to deal with new types of attacks and unknown threats. To improve the security level of online education, the paper proposes an information security detection method for online education based on improved clustering detection algorithm and software-defined wireless sensor network to improve the detection accuracy. The study combines the improved clustering detection algorithm and software-defined wireless sensor network technology to construct a novel network security detection model. In the comparison test of the improved clustering detection algorithm, it is found that the algorithm has the lowest training error of 0.040 and 0.031 in the test and training sets, which are both better than the comparison algorithm. Subsequent empirical analysis of the network security detection model shows that the research proposed detection accuracy and detection time are 90.3% and 2.6s respectively, which are significantly better than the other models. The above data validate that the designed novel network security detection model can better safeguard the information security and user data privacy of online network education. The study not only improves the information security level of online education, but also provides strong support for promoting its healthy development.
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DOI: https://doi.org/10.31449/inf.v48i21.6257
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