Phishing website detection using ensemble learning models

A malicious website, often known as a malicious URL, is a platform considering hosting unwanted content including spam, harmful advertisements, and dangerous websites. It is crucial towards quickly identify dangerous URLs. Blacklisting, regular expression, & signature matching techniques have all been employed in earlier investigations. These methods are utterly useless considering identifying new URLs, malicious URL variants, or URLs that have never been seen before. Machine learning-based solution that has been suggested can help towards solve this problem. Considering this kind about solution, in-depth study about feature engineering & feature representation about security artifact types, such as URLs, is necessary. Additionally, resources considering feature engineering & feature representation must be continuously improved towards support variations about current URLs or completely new URLs. Deep learning, machine learning, & artificial intelligence (AI) systems have recently been used towards achieve human-level performance in a number about areas & even surpass human eyesight in a number about computer vision applications. They can automatically extract best feature representation from raw inputs. We propose various algorithms, including SVM, Random forest, XgBoost, & AdaBoost, towards capitalise on & turn performance increase about them into cyber security area.