Leveraging Machine Learning to Combat Phishing Threats

Creating and testing a phishing detection system for websites that is based on machine learning is the main goal of this research project. By precisely identifying fake websites intended to steal confidential user data, this method seeks to improve cybersecurity precautions. A methodical technique is used to accomplish the goals. To do this, a dataset of reputable and well-known phishing websites must be assembled. To train and test detection models, a variety of machine learning methods are used, including random forests, decision trees, KNN, and Naive Bayes. Interestingly, certain algorithms like random forests perform better than others at telling the difference between trustworthy and phony websites. The efficiency of the system is also greatly influenced by feature extraction and selection strategies, with some characteristics being more helpful in accurately classifying data than others. Additionally, the need for constant research to improve web users’ cybersecurity defenses is highlighted by the emphasis on model adaptation that is made on a continuous basis to handle developing phishing strategies.