REAL TIME SPEAR PHISHING MAIL DETECTION USING AI & ML

Abstract

Spear phishing continues to pose a serious cybersecurity risk, leveraging highly tailored and deceptive emails to exploit individuals and organizations. Traditional email security systems often fall short in identifying these advanced threats, emphasizing the need for intelligent, AI-powered solutions. This study proposes a real-time detection framework combining Random Forest (RF), Long Short-Term Memory (LSTM), and Support Vector Machines (SVM). RF aids in feature selection and classification, LSTM captures the sequential flow of email content, and SVM identifies subtle anomalies in text. By analyzing metadata, linguistic features, and embedded links, the model effectively detects malicious emails with high accuracy and reduced false positives. While the approach significantly strengthens email security, it also acknowledges ongoing challenges like adaptive phishing tactics and adversarial threats. Overall, this research highlights the promise of AI in combating spear phishing and lays the groundwork for next-generation email security solutions.