A System Review on Fraudulent Website Detection Using Machine Learning Technique

At present, scams and malicious websites are one of the most widespread and dangerous problems on the website. It brings enormous economic suffering and irretrievable losses to companies and individuals. This approach can strengthen the Internet’s legitimacy and impose sanctions on criminals who engage in prohibited or malicious activities. However, governments still need a derivation to classify websites as dangerous or non-dangerous. However, several malicious and counterfeit goods are published on fraudulent websites to cheat consumers and make high and unfair profits. Due to the proliferation of such fraudulent websites, it is difficult to detect and identify them through manual inspection. Phishing attacks include various attacks, including spoofing malicious-based, DNS-based, data theft, email/spam, web-based delivery, and telephone-based phishing. We propose an integrated machine learning (ML) framework for fraudulent website detection to solve this problem. Artificial neural networks (ANN), support vector machine (SVM), random forests (RF), and K-nearest neighbor (K-NN) are algorithms to detect phishing websites accurately. Some URLs can be used to classify them as appropriate or phishing. Data from publicly available phishing websites can be collected from the UCIrvine ML repository for training and testing. Then, the results can be predicted using the features of the dataset. We conduct an in-depth literature review and propose methods for detecting phishing websites using ML methods.