{"title":"从品牌的制高点打击网络钓鱼","authors":"V. Bulakh, Minaxi Gupta","doi":"10.1145/2875475.2875478","DOIUrl":null,"url":null,"abstract":"Most anti-phishing solutions that exist today require scanning a large portion of the web, which is vast and equivalent to finding a needle in a haystack. In addition, such solutions are not very efficient. We propose a different approach. Our solution does not rely on the scanning of the entire Internet or a large portion of it and only needs access to the brand's traffic in order to be able to detect phishing attempts against that brand. By analyzing a sample of phishing websites, we find features that can be used to distinguish phishing websites from the legitimate ones. We then use these features to train a machine learning classifier capable of helping brands detect phishing attempts against them. Our approach can detect up to 86% of phishing attacks against the brands and is best used as a complementary tool to the existing anti-phishing solutions.","PeriodicalId":393015,"journal":{"name":"Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Countering Phishing from Brands' Vantage Point\",\"authors\":\"V. Bulakh, Minaxi Gupta\",\"doi\":\"10.1145/2875475.2875478\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Most anti-phishing solutions that exist today require scanning a large portion of the web, which is vast and equivalent to finding a needle in a haystack. In addition, such solutions are not very efficient. We propose a different approach. Our solution does not rely on the scanning of the entire Internet or a large portion of it and only needs access to the brand's traffic in order to be able to detect phishing attempts against that brand. By analyzing a sample of phishing websites, we find features that can be used to distinguish phishing websites from the legitimate ones. We then use these features to train a machine learning classifier capable of helping brands detect phishing attempts against them. Our approach can detect up to 86% of phishing attacks against the brands and is best used as a complementary tool to the existing anti-phishing solutions.\",\"PeriodicalId\":393015,\"journal\":{\"name\":\"Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2875475.2875478\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2875475.2875478","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Most anti-phishing solutions that exist today require scanning a large portion of the web, which is vast and equivalent to finding a needle in a haystack. In addition, such solutions are not very efficient. We propose a different approach. Our solution does not rely on the scanning of the entire Internet or a large portion of it and only needs access to the brand's traffic in order to be able to detect phishing attempts against that brand. By analyzing a sample of phishing websites, we find features that can be used to distinguish phishing websites from the legitimate ones. We then use these features to train a machine learning classifier capable of helping brands detect phishing attempts against them. Our approach can detect up to 86% of phishing attacks against the brands and is best used as a complementary tool to the existing anti-phishing solutions.