Fake Website Detection Using Machine Learning & ANN

Abstract

Day after day the number of internet users increases, phishing has grown increasingly breakneck. Phishing attacks pose a serious threat to people’s daily lives and the online environment. For example, the attacker poses as a trustworthy source in order to get sensitive information or the victim’s digital identity, such as a credit card number or certificate or other valuable information. For this reason, people lose their identity after falling into the trap of these raiders. As the name implies, phishing or faking sites are false copies of actual web sites. When a person’s identification card gets stolen, they are cheating. To create the website for this paper debate publishing, we will be relying on a machine learning algorithm, Neural Network Classifier MLPC (Multilayer perceptron Classifier) and have differentiated the percentage of accuracy between them. We have used five machine learning algorithms: Naive Bayes algorithm, K-nearest neighbors (KNN), SVM, Decision tree, Random forest algorithm. Most accurate and well directed perspective of this approach may be found in our dataset that it’s a scam or fake website. Among them, the Random Forest algorithm provided 97.9 % accuracy.