• N.RAHUL PAL
• Mr.S.Vamsi

- The Internet is a crucial part of our life. Internet customers may be affected by different sorts of cyber threats. Thus, cyber threats may additionally attack monetary statistics, non-public statistics, online banking, and e-trade. Phishing is a form of cyber threat that is focused on getting non-public information such as credit card statistics and social safety numbers. This undertaking proposes a unique technique for detecting phishing websites with the usage of an Extreme Learning Machine (ELM). The ELM is a gadget learning set of rules that is acknowledged for its rapid-getting-to-know velocity and precise generalization performance. We also compared the overall performance metrics of ELM with SVM, Gradient Boosting Classifier, and Gaussian Naïve Bayes. The proposed approach uses a function extraction approach to extract crucial capabilities from the URLs of websites. The extracted features are then fed into the ELM classifier to differentiate between legitimate and phishing websites. The technique evaluates the use of a dataset of real-international phishing websites and valid websites, and the outcomes display that the proposed approach outperforms numerous other latest phishing internet site detection strategies. Overall, this assignment gives a promising technique for detecting phishing websites with the usage of the ELM set of rules. The proposed approach may be beneficial in improving the security of online customers by using preventing them from gaining access to phishing websites and protecting their touchy data.

Keywords: Internet, Phishing, ELM, SVM, Gradient Boosting Classifier, Naïve Bayes, Legitimate.

"A Framework for Detecting Phishing Website by using Extreme Learning Machine Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.b762-b767, April-2024, Available :http://www.jetir.org/papers/JETIR2404186.pdf

"A Framework for Detecting Phishing Website by using Extreme Learning Machine Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppb762-b767, April-2024, Available at : http://www.jetir.org/papers/JETIR2404186.pdf