Intelligent technical web-based approach leveraging web scrapper and random forest algorithm to detect phishing emails and sms

Abstract

Systems and processes are disclosed for detecting phishing emails and text messages. The method involves accessing the internet to gather data from various online sources, executing multi-threaded downloaders to handle multiple data streams, and storing the downloaded data in a repository. A web scraping agent analyzes and extracts relevant features from the stored data, transforming unstructured data into a structured data model. Both are stored in a database. An after-processing dataset is generated, including testing and training datasets for machine learning analysis. Random Forest models are evaluated to determine accuracy in predicting phishing attempts, and optimal models are selected, which generate phishing predictions from new data, with feature extraction identifying attributes relevant for detection. An evaluation model assesses feature extraction accuracy and overall system performance. The machine learning algorithm adapts to new phishing techniques. The trained model is integrated into a security infrastructure, with real-time processing and continuous loop feedback.