{"title":"基于人工智能的网络安全提升技术的实证评价","authors":"Chhaya Nayak, Chintala Lakshmana Rao, Tanweer Alam, Shalini Singh, Shaziya Islam, Umme Habiba Maginmani","doi":"10.1109/ICEARS56392.2023.10085368","DOIUrl":null,"url":null,"abstract":"Artificial intelligence (AI) technologies have been recently developed, and their advantages can be observed in a wide range of domains, from image processing to face detection. AI-based approaches can assist opponents to enhance their attack strategies while also improving cyber protection technologies. The efficiency of AI techniques against cyber security issues are evaluated. Here, the primary data and a quantitative study design approach is used. The information is then obtained from software industry professionals. The sample size for this study is 549 people, and it included confirmatory factor analysis, discriminant validity testing, model fundamental analysis, and hypothesis evaluation. All variables' P-values were found to be statistical, with the exception of intelligent agents, which had no meaningful relationship to AI or cyber security. The main issues were availability, geographical locations, study population, and other related variables.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Empirical Evaluation of Artificial Intelligence-based Techniques for Improving Cyber Security\",\"authors\":\"Chhaya Nayak, Chintala Lakshmana Rao, Tanweer Alam, Shalini Singh, Shaziya Islam, Umme Habiba Maginmani\",\"doi\":\"10.1109/ICEARS56392.2023.10085368\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial intelligence (AI) technologies have been recently developed, and their advantages can be observed in a wide range of domains, from image processing to face detection. AI-based approaches can assist opponents to enhance their attack strategies while also improving cyber protection technologies. The efficiency of AI techniques against cyber security issues are evaluated. Here, the primary data and a quantitative study design approach is used. The information is then obtained from software industry professionals. The sample size for this study is 549 people, and it included confirmatory factor analysis, discriminant validity testing, model fundamental analysis, and hypothesis evaluation. All variables' P-values were found to be statistical, with the exception of intelligent agents, which had no meaningful relationship to AI or cyber security. The main issues were availability, geographical locations, study population, and other related variables.\",\"PeriodicalId\":338611,\"journal\":{\"name\":\"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEARS56392.2023.10085368\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEARS56392.2023.10085368","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Empirical Evaluation of Artificial Intelligence-based Techniques for Improving Cyber Security
Artificial intelligence (AI) technologies have been recently developed, and their advantages can be observed in a wide range of domains, from image processing to face detection. AI-based approaches can assist opponents to enhance their attack strategies while also improving cyber protection technologies. The efficiency of AI techniques against cyber security issues are evaluated. Here, the primary data and a quantitative study design approach is used. The information is then obtained from software industry professionals. The sample size for this study is 549 people, and it included confirmatory factor analysis, discriminant validity testing, model fundamental analysis, and hypothesis evaluation. All variables' P-values were found to be statistical, with the exception of intelligent agents, which had no meaningful relationship to AI or cyber security. The main issues were availability, geographical locations, study population, and other related variables.