{"title":"一个钓鱼网站黑名单生成器","authors":"M. Sharifi, Seyed Hossein Siadati","doi":"10.1109/AICCSA.2008.4493625","DOIUrl":null,"url":null,"abstract":"Phishing is an increasing web attack both in volume and techniques sophistication. Blacklists are used to resist this type of attack, but fail to make their lists up- to-date. This paper proposes a new technique and architecture for a blacklist generator that maintains an up-to-date blacklist of phishing sites. When a page claims that it belongs to a given company, the company's name is searched in a powerful search engine like Google. The domain of the page is then compared with the domain of each of the Google's top- 10 searched results. If a matching domain is found, the page is considered as a legitimate page, and otherwise as a phishing site. Preliminary evaluation of our technique has shown an accuracy of 91% in detecting legitimate pages and 100% in detecting phishing sites.","PeriodicalId":234556,"journal":{"name":"2008 IEEE/ACS International Conference on Computer Systems and Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"44","resultStr":"{\"title\":\"A phishing sites blacklist generator\",\"authors\":\"M. Sharifi, Seyed Hossein Siadati\",\"doi\":\"10.1109/AICCSA.2008.4493625\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Phishing is an increasing web attack both in volume and techniques sophistication. Blacklists are used to resist this type of attack, but fail to make their lists up- to-date. This paper proposes a new technique and architecture for a blacklist generator that maintains an up-to-date blacklist of phishing sites. When a page claims that it belongs to a given company, the company's name is searched in a powerful search engine like Google. The domain of the page is then compared with the domain of each of the Google's top- 10 searched results. If a matching domain is found, the page is considered as a legitimate page, and otherwise as a phishing site. Preliminary evaluation of our technique has shown an accuracy of 91% in detecting legitimate pages and 100% in detecting phishing sites.\",\"PeriodicalId\":234556,\"journal\":{\"name\":\"2008 IEEE/ACS International Conference on Computer Systems and Applications\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-03-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"44\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE/ACS International Conference on Computer Systems and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AICCSA.2008.4493625\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE/ACS International Conference on Computer Systems and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AICCSA.2008.4493625","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Phishing is an increasing web attack both in volume and techniques sophistication. Blacklists are used to resist this type of attack, but fail to make their lists up- to-date. This paper proposes a new technique and architecture for a blacklist generator that maintains an up-to-date blacklist of phishing sites. When a page claims that it belongs to a given company, the company's name is searched in a powerful search engine like Google. The domain of the page is then compared with the domain of each of the Google's top- 10 searched results. If a matching domain is found, the page is considered as a legitimate page, and otherwise as a phishing site. Preliminary evaluation of our technique has shown an accuracy of 91% in detecting legitimate pages and 100% in detecting phishing sites.