%0 Journal Article %1 P__2015 %A Akare, Pranali P. %A Mohm, Heena. %A Maniyar, H. %A Thorat, Tejendra D. %A k. Pagar, Jagruti %D 2015 %I Auricle Technologies, Pvt., Ltd. %J International Journal on Recent and Innovation Trends in Computing and Communication %K Bayes classifier data detection fusion page phishing theory web %N 1 %P 148--151 %R 10.17762/ijritcc2321-8169.150131 %T Detecting Phishing Web Pages using NB classifier and EMD Approach %U http://dx.doi.org/10.17762/ijritcc2321-8169.150131 %V 3 %X Phishing is online security attack where attacker create replica of exiting web page for accessing users password, personal, financial data. Phishing is a form of online fraudulent activity in which an attacker aims to steal a victim’s personal information, such as a bank account, online banking password or a credit card number. Targets are tricked into providing such information by a combination of spoofing techniques and social engineering. We have proposed a new approach named as Änti phishing framework using Bayesian approach for content based phishing web page detection. Our model used to detect the similarity between suspicious web page and secure web page through image and text contained by the web page. Text classifier, an image classifier and fusion algorithm the result from classifier are introduced. In the text classifier naive Bayes algorithm is used to calculate the Probability, an image classifier the earth mover’s distance algorithm is used to measure the visual Similarity and our Bayesian model is designed to determine the threshold. In data fusion detection for image classifier and text classifier means how many web site image and text are matched exactly. If any web page contains above 50% spam text and image so we are declare these web sites are phishing other-wise not phishing

@article{P__2015, abstract = {Phishing is online security attack where attacker create replica of exiting web page for accessing users password, personal, financial data. Phishing is a form of online fraudulent activity in which an attacker aims to steal a victim’s personal information, such as a bank account, online banking password or a credit card number. Targets are tricked into providing such information by a combination of spoofing techniques and social engineering. We have proposed a new approach named as "Anti phishing framework using Bayesian approach for content based phishing web page detection. Our model used to detect the similarity between suspicious web page and secure web page through image and text contained by the web page. Text classifier, an image classifier and fusion algorithm the result from classifier are introduced. In the text classifier naive Bayes algorithm is used to calculate the Probability, an image classifier the earth mover’s distance algorithm is used to measure the visual Similarity and our Bayesian model is designed to determine the threshold. In data fusion detection for image classifier and text classifier means how many web site image and text are matched exactly. If any web page contains above 50% spam text and image so we are declare these web sites are phishing other-wise not phishing}, added-at = {2015-08-03T08:22:23.000+0200}, author = {Akare, Pranali P. and Mohm, Heena. and Maniyar, H. and Thorat, Tejendra D. and k. Pagar, Jagruti}, biburl = {https://www.bibsonomy.org/bibtex/238339766d0c474e9c6664f06db1c5cb2/ijritcc}, doi = {10.17762/ijritcc2321-8169.150131}, interhash = {b38a7f1c44807de0c57aa1bdfaf6fb61}, intrahash = {38339766d0c474e9c6664f06db1c5cb2}, journal = {International Journal on Recent and Innovation Trends in Computing and Communication}, keywords = {Bayes classifier data detection fusion page phishing theory web}, month = {january}, number = 1, pages = {148--151}, publisher = {Auricle Technologies, Pvt., Ltd.}, timestamp = {2015-08-03T08:22:23.000+0200}, title = {Detecting Phishing Web Pages using {NB} classifier and {EMD} Approach}, url = {http://dx.doi.org/10.17762/ijritcc2321-8169.150131}, volume = 3, year = 2015 }