Machine Learning Based Phishing Website Detection System - Natural Language Processing Approach

Natural language processing (NLP) methods can be used to identify phishing websites in addition to static and dynamic features. Phishing sites frequently include certain phrases, misspellings, or misleading text patterns in their material, as well as other visual cues and language that match those of authentic websites. Through the examination of textual components such anchor texts, meta-descriptions, and homepage titles. The proposed model is able to comprehend the linguistic traits that are frequently seen in phishing websites using NLP-based features like sentiment analysis, keyword matching, and phrase frequency to increase model's sensitivity. In addition, we use real-time URL parsing to detect possible dangers as users come across them, guaranteeing prompt notifications and lowering the possibility that phishing attempts would be successful. Our study emphasizes how crucial it is to keep improving and upgrading phishing detection models because attackers usually change their strategies.