AI/ML-Driven Phishing Defence: Crafting a Robust Email Security Framework

Abstract-Phishing attacks, which use people’s vulnerability to trick them into disclosing personal information, continue to be the most widespread threat type, at least for the time being. These types of attacks typically involve phony emails purporting to be from reputable sources, such as banks, companies, or government buildings. Even though standard email filtering methods are somewhat helpful in the fight against phishing, they are very unlikely to detect sophisticated phishing channels like spear phishing and zero-day phishing that are used today. As a result, using machine learning (ML) and artificial intelligence (AI) to address email security is becoming more popular. They can learn from sampled volumes of emails and use that knowledge to better identify phishing and non-phishing emails. Here, we suggest developing a phishing detection system with AI/ML, which will be instrumentally essential to ensuring dependable and flexible email security. To categorize emails according to features taken from the subject, body, and links of the emails, the system uses Random Forest, Support Vec- tor Machines (SVM), and Neural Networks. We trained and evaluated these models to determine the feasibility of phishing identification using both phishing and benign email corpora. The study’s accomplishments included a higher detection accuracy in comparison to traditional methods and a further decrease in misrecognition, both of which enhance security overall. Notably, the suggested system is robust and adaptable to sophisticated phishing attacks by combining a multi-model approach with learning mechanisms.