Phishing Detection Using Artificial Intelligence

  • Kuldeep Charan, Dr. Veeresh G. Kasabegoudar
Keywords: Phishing Detection (PD), Chrome Extension (CE), Random Forest (RF), Support Vector Machine (SVM), Neural Net-works (NN), Confusion Matrix.


The essential target of our endeavor is to do the machine learning technique to detect Phishing and Malicious websites. The end solution or the result of this project will be a software product which will help in detecting the phishing and malicious websites. Phishing is a technique to extract user credentials and sensitive data by the phishers. The phishing website is usually designed in such a way that they mirror the actual legit website, where not so regular users miss to identify whether the website is malicious or phishing blacklisting the known phishing websites or check the attributes in the phishing page whether it contains any malicious codes. This process has poor accuracy and very low adaptability to new phishing links. That is the reason that we have designed this project where we generated an idea to implement machine learning technique by using some classification algorithms and comparing these algorithms with our existing data set. we'll test assessments, for instance, SVT, Decision tree, and Neural relationship on a dataset of phishing joins from the UCI Machine Learning account, and pick the simplest model to support a program module, which may be flowed as a chrome advancement. The phishing is standard technique that duplicates trustful URL and site page links. The major need is to style AI type on a dataset created for phishing avoidance.

How to Cite
Dr. Veeresh G. Kasabegoudar, K. C. (2021). Phishing Detection Using Artificial Intelligence. Design Engineering, 8424-8436. Retrieved from