SecureMind: Detect Online Phishing Using Machine Learning Techniques

Abstract

The proliferation of online scams, phishing, and AIgenerated fraudulent content demands robust solutions to protect users in the digital space. This paper presents SecureMind, a Machine Learning (ML) model that detects scams in various types of content, including emails, reviews, job postings, and news articles. SecureMind employs Random Forest (RF) classifiers trained on curated datasets to achieve high detection accuracy. SecureMind is implemented as a browser extension for realtime protection and it provides interpretable decision making. An interactive gamified training module is added to SecureMind to improve awareness of online phishing tactics. Experimental results demonstrate the effectiveness of the model in classifying fraudulent content and it outperforms existing solutions.