Machine Learning Algorithms for Detecting Phishing Websites

Despite the quick growth of the digital world, phishing attempts still constitute a serious threat to the security of online banking transactions. The goal of this research project is to evaluate the usage of machine learning algorithms to detect websites that are used to spread phishing messages. This work specifically considers complex feature engineering and algorithm selection approaches. The detection method is enhanced by the application of optimal model, which is utilized to extract and evaluate text-based elements from phishing websites. It is done with optimal model. DNSPython and Python-Whois are the technologies utilized to finish gathering domain-related data. However, Scikit-learn makes the process of putting machine learning models into practice easier. The objectives that AutoML aids in achieving include efficient model selection and optimization. This has the important advantage of making it possible to automatically identify the algorithms with the highest performance levels. The objective of this research endeavour is to enhance the accuracy and robustness of phishing detection systems through the utilization of diverse methodologies and technologies. Additionally, the study makes an effort to clarify how machine learning would be able to successfully counteract the increasingly complex phishing methods. Through improving the automatic identification of phishing-related websites, our program seeks to contribute to the creation of safer online environments.