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Research on defect detection of media information security based on genetic algorithm

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Abstract

Security defect detection is a dynamic security protection technology, which can defend against internal and external attacks of network media information. Based on this, a detection method for media information security defect based on genetic algorithm is studied. The distribution model of media information security defect in complex network environment is constructed by signal processing method. The threat index and host threat index which affect media information security in network attack environment are simulated by ARMA model. The defect index of media information security is obtained, which is defined as media information invaded by virus when the index is higher than the set threshold. The weights of the neural network are transformed into genetic neural network to detect the relevance of the extracted virus-invaded media information stream, and the membership characteristics of the media information infected by the virus are obtained. Based on this, the security defects of the media information can be judged and the accurate detection of the media information security defects can be realized. The simulation results show that the method can effectively detect the defects of media information security, and the detection accuracy rate reaches more than 90%, which is significantly better than the contrast method.

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Acknowledgements

Research project of Hospital (T.C.M) Affiliated to Southwest Medical University: hospital micro-video transmission mode and effect analysis; Luzhou soft science research project—current situation of hospital image micro-video communication and optimization strategy analysis (No. 19RKX0006).

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  1. Propaganda United Front Work Department, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, China

    Fan Wang

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  1. Fan Wang
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Correspondence to Fan Wang.

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Wang, F. Research on defect detection of media information security based on genetic algorithm. Appl Nanosci 13, 2439–2447 (2023). https://doi.org/10.1007/s13204-021-02137-3

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  • DOI: https://doi.org/10.1007/s13204-021-02137-3

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