%0 Journal Article %1 jayshreehajgude2013performance %A Jayshree Hajgude, Dr. Lata Ragha %D 2013 %E Hope, Dr.Martin %J Int. J. on Recent Trends in Engineering and Technology, %K DNS Phishing URL lexical %N 1 %P 7 %T Performance Evaluation of Phish Mail Guard: Phishing Mail Detection Technique by using Textual and URL analysis” %U http://searchdl.org/public/journals/2013/IJRTET/8/1/61.pdf %V 8 %X Phishing emails usually contain a message from a credible looking source requesting a user to click a link to a website where user is asked to enter a password or other confidential information. M ost phishing emails aim at withdrawing money from financial institutions or getting access to private information. Phishing has increased enormously over the last years and is a serious threat to global security and economy. Phishing attacks are becoming more frequent and sophisticated. There are a number of possible countermeasures to phishing. A number of anti-phishing solutions have been proposed to date. Some approaches attempt to solve the phishing problem at the e-mail level. A technique must be capable of determining whether an email is legitimate or a phishing, given only the URL and the email content. URL and textual content analysis of email will results in a highly accurate anti phishing email classifier. We proposed a technique where we considered the advantages of blacklist, white list and heuristic technique for increasing accuracy and reducing false positive rate. In heuristic technique we are using textual analysis and URL analysis of e-mail. Since most of the phishing mails have similar contents, our proposed method increased the performance by analyzing textual contents of mail and lexical URL analysis. This technique detect phishing mail if DNS in actual link is present in blacklist.DNS is present in white list then it is considered as legitimate DNS. If it is not present in blacklist as well as white list then it is analyzed by comparing senders DNS and DNS exists in link. This method analyzes URL with the help of lexical features of URL. Contents of mails are also analyzed because most of the phishing mail has similar contents. With the help blacklist and white list we are avoiding detection time for phishing and legitimate email. At the same time we are decreasing false positive rate by combining features of DNS, textual content analysis of email and URL analysis.

@article{jayshreehajgude2013performance, abstract = {Phishing emails usually contain a message from a credible looking source requesting a user to click a link to a website where user is asked to enter a password or other confidential information. M ost phishing emails aim at withdrawing money from financial institutions or getting access to private information. Phishing has increased enormously over the last years and is a serious threat to global security and economy. Phishing attacks are becoming more frequent and sophisticated. There are a number of possible countermeasures to phishing. A number of anti-phishing solutions have been proposed to date. Some approaches attempt to solve the phishing problem at the e-mail level. A technique must be capable of determining whether an email is legitimate or a phishing, given only the URL and the email content. URL and textual content analysis of email will results in a highly accurate anti phishing email classifier. We proposed a technique where we considered the advantages of blacklist, white list and heuristic technique for increasing accuracy and reducing false positive rate. In heuristic technique we are using textual analysis and URL analysis of e-mail. Since most of the phishing mails have similar contents, our proposed method increased the performance by analyzing textual contents of mail and lexical URL analysis. This technique detect phishing mail if DNS in actual link is present in blacklist.DNS is present in white list then it is considered as legitimate DNS. If it is not present in blacklist as well as white list then it is analyzed by comparing senders DNS and DNS exists in link. This method analyzes URL with the help of lexical features of URL. Contents of mails are also analyzed because most of the phishing mail has similar contents. With the help blacklist and white list we are avoiding detection time for phishing and legitimate email. At the same time we are decreasing false positive rate by combining features of DNS, textual content analysis of email and URL analysis.}, added-at = {2014-02-03T07:22:51.000+0100}, author = {Jayshree Hajgude, Dr. Lata Ragha}, biburl = {https://www.bibsonomy.org/bibtex/2d656ce3998af7e3d50384bb2fdfca81b/idescitation}, editor = {Hope, Dr.Martin}, interhash = {e7f99f29a41093573c1025d36b62c701}, intrahash = {d656ce3998af7e3d50384bb2fdfca81b}, journal = {Int. J. on Recent Trends in Engineering and Technology,}, keywords = {DNS Phishing URL lexical}, month = {January}, number = 1, pages = 7, timestamp = {2014-02-03T07:22:51.000+0100}, title = {Performance Evaluation of Phish Mail Guard: Phishing Mail Detection Technique by using Textual and URL analysis”}, url = {http://searchdl.org/public/journals/2013/IJRTET/8/1/61.pdf}, volume = 8, year = 2013 }