• Neetu singh Rathore

Phishing is when cybercriminal send malicious email designed to trick people into falling for a scam. The intent is often to get users to reveal financial information, system credentials, or other sensitive data. In phishing process attackers used fake websites page known as phishing page which looks like same as real websites, from that attackers theft all sensitive and private data of the banking websites and any other financial websites data. Phishing web pages used for theft user name, password and account detail of social networking sites. To secure websites many anti-phishing methods are applied. In this paper JRip, J48, Naïve Bayes (NB) and BayesNet algorithms are applied in Weka for phishing website detection using data mining classification model. There are 31 attributes are used in different algorithms to show terms and error rates with accuracy. J48 algorithm gives best results in all algo with minimum error rate

Phishing Websites,Machine learning technique, Jrip algo, J48, Naive Bayes algo, Bayes net algo

"Detection of Phishing Websites Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.158-163, June 2019, Available :http://www.jetir.org/papers/JETIR1907620.pdf

"Detection of Phishing Websites Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp158-163, June 2019, Available at : http://www.jetir.org/papers/JETIR1907620.pdf