{"title":"基于PSOAANN的单类分类器的网络钓鱼检测","authors":"M. Pandey, V. Ravi","doi":"10.1109/ICETET.2013.46","DOIUrl":null,"url":null,"abstract":"We propose to detect phishing emails and websites using particle swarm optimization (PSO) trained auto associative neural network (PSOAANN), which is employed as one class classifier. PSOAANN achieved better results when compared to previous efforts. In the study, we also developed a new feature selection method based on the weights from input to hidden layers of the PSOAANN. We compared its performance with other methods.","PeriodicalId":440967,"journal":{"name":"2013 6th International Conference on Emerging Trends in Engineering and Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Phishing Detection Using PSOAANN Based One-Class Classifier\",\"authors\":\"M. Pandey, V. Ravi\",\"doi\":\"10.1109/ICETET.2013.46\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose to detect phishing emails and websites using particle swarm optimization (PSO) trained auto associative neural network (PSOAANN), which is employed as one class classifier. PSOAANN achieved better results when compared to previous efforts. In the study, we also developed a new feature selection method based on the weights from input to hidden layers of the PSOAANN. We compared its performance with other methods.\",\"PeriodicalId\":440967,\"journal\":{\"name\":\"2013 6th International Conference on Emerging Trends in Engineering and Technology\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 6th International Conference on Emerging Trends in Engineering and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICETET.2013.46\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 6th International Conference on Emerging Trends in Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICETET.2013.46","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Phishing Detection Using PSOAANN Based One-Class Classifier
We propose to detect phishing emails and websites using particle swarm optimization (PSO) trained auto associative neural network (PSOAANN), which is employed as one class classifier. PSOAANN achieved better results when compared to previous efforts. In the study, we also developed a new feature selection method based on the weights from input to hidden layers of the PSOAANN. We compared its performance with other methods.
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