Phishing website detection using ensemble learning models
Needa Iffath, Upendra Kumar Mummadi, Fahmina Taranum, Syed Shabbeer Ahmad, Imtiyaz Khan, Imtiyaz Khan, D. Shravani · AIP conference proceedings · 2024
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, indepth 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.