S. Velliangiri, Iwin Thanakumar Joseph, Shanthini Pandiaraj, P. Leela Jancy, Ch. Madhubabu
{"title":"使用基于Jaya优化的遗传算法的物联网环境增强安全框架","authors":"S. Velliangiri, Iwin Thanakumar Joseph, Shanthini Pandiaraj, P. Leela Jancy, Ch. Madhubabu","doi":"10.1504/ijitst.2023.127388","DOIUrl":null,"url":null,"abstract":"The internet of things (IoT) employs a cloud network, and the data stored in the cloud servers are highly vulnerable to various attacks. As per the current analysis report, around 23% of IoT devices are prone to attack. The data stored in the cloud storage are highly vulnerable to attacks leading to a pullback factor of 15% in economic growth. Considering the above security of the IoT devices, this paper proposes a framework integrating the Jaya algorithm and genetic algorithm to achieve an optimal detection of intrusion in the IoT network. The JA is a parameter less algorithm that does not require any precise control parameters. In contrast, the GA is a meta-heuristic approach that produces reasonable quality solutions for complex functions. The extensive analysis of the proposed algorithm yield better performance in vital parameters like accuracy, recall and F-score.","PeriodicalId":38357,"journal":{"name":"International Journal of Internet Technology and Secured Transactions","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An enhanced security framework for IoT environment using Jaya optimisation-based genetic algorithm\",\"authors\":\"S. Velliangiri, Iwin Thanakumar Joseph, Shanthini Pandiaraj, P. Leela Jancy, Ch. Madhubabu\",\"doi\":\"10.1504/ijitst.2023.127388\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The internet of things (IoT) employs a cloud network, and the data stored in the cloud servers are highly vulnerable to various attacks. As per the current analysis report, around 23% of IoT devices are prone to attack. The data stored in the cloud storage are highly vulnerable to attacks leading to a pullback factor of 15% in economic growth. Considering the above security of the IoT devices, this paper proposes a framework integrating the Jaya algorithm and genetic algorithm to achieve an optimal detection of intrusion in the IoT network. The JA is a parameter less algorithm that does not require any precise control parameters. In contrast, the GA is a meta-heuristic approach that produces reasonable quality solutions for complex functions. The extensive analysis of the proposed algorithm yield better performance in vital parameters like accuracy, recall and F-score.\",\"PeriodicalId\":38357,\"journal\":{\"name\":\"International Journal of Internet Technology and Secured Transactions\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Internet Technology and Secured Transactions\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijitst.2023.127388\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Internet Technology and Secured Transactions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijitst.2023.127388","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
An enhanced security framework for IoT environment using Jaya optimisation-based genetic algorithm
The internet of things (IoT) employs a cloud network, and the data stored in the cloud servers are highly vulnerable to various attacks. As per the current analysis report, around 23% of IoT devices are prone to attack. The data stored in the cloud storage are highly vulnerable to attacks leading to a pullback factor of 15% in economic growth. Considering the above security of the IoT devices, this paper proposes a framework integrating the Jaya algorithm and genetic algorithm to achieve an optimal detection of intrusion in the IoT network. The JA is a parameter less algorithm that does not require any precise control parameters. In contrast, the GA is a meta-heuristic approach that produces reasonable quality solutions for complex functions. The extensive analysis of the proposed algorithm yield better performance in vital parameters like accuracy, recall and F-score.