{"title":"人工智能技术在计算机网络安全通信中的应用","authors":"Fu-lei Li","doi":"10.1155/2022/9785880","DOIUrl":null,"url":null,"abstract":"In order to cope with the frequent challenges of network security issues, a method of applying artificial intelligence technology to computer network security communication is proposed. First, within the framework of computer network communication, an intelligent protocol reverse analysis method is proposed. By converting the protocol into an image and establishing a convolutional neural network model, artificial intelligence technology is used to map the data to the protocol result. Finally, use the model to test the test data to adjust the model parameters and optimize the model as much as possible. The experimental results show that compared with the test model, the results obtained after training with the deep convolutional neural network model in this paper have increased the accuracy by 2.4%, reduced the loss by 38.2%, and reduced the running time by 42 times. The correctness and superiority of the algorithm and model are verified.","PeriodicalId":46052,"journal":{"name":"Journal of Control Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Application of Artificial Intelligence Technology in Computer Network Security Communication\",\"authors\":\"Fu-lei Li\",\"doi\":\"10.1155/2022/9785880\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to cope with the frequent challenges of network security issues, a method of applying artificial intelligence technology to computer network security communication is proposed. First, within the framework of computer network communication, an intelligent protocol reverse analysis method is proposed. By converting the protocol into an image and establishing a convolutional neural network model, artificial intelligence technology is used to map the data to the protocol result. Finally, use the model to test the test data to adjust the model parameters and optimize the model as much as possible. The experimental results show that compared with the test model, the results obtained after training with the deep convolutional neural network model in this paper have increased the accuracy by 2.4%, reduced the loss by 38.2%, and reduced the running time by 42 times. The correctness and superiority of the algorithm and model are verified.\",\"PeriodicalId\":46052,\"journal\":{\"name\":\"Journal of Control Science and Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2022-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Control Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2022/9785880\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Control Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2022/9785880","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Application of Artificial Intelligence Technology in Computer Network Security Communication
In order to cope with the frequent challenges of network security issues, a method of applying artificial intelligence technology to computer network security communication is proposed. First, within the framework of computer network communication, an intelligent protocol reverse analysis method is proposed. By converting the protocol into an image and establishing a convolutional neural network model, artificial intelligence technology is used to map the data to the protocol result. Finally, use the model to test the test data to adjust the model parameters and optimize the model as much as possible. The experimental results show that compared with the test model, the results obtained after training with the deep convolutional neural network model in this paper have increased the accuracy by 2.4%, reduced the loss by 38.2%, and reduced the running time by 42 times. The correctness and superiority of the algorithm and model are verified.
期刊介绍:
Journal of Control Science and Engineering is a peer-reviewed, open access journal that publishes original research articles as well as review articles in all areas of control science and engineering.