Improving Email Security Through Machine Learning-Based Phishing Attack Detection

Phishing remains a persistent cybersecurity threat, necessitating innovative solutions for timely detection and prevention. Email security is critical to organizations of all types, with email comprising around 80% of official communication globally. Attackers exploit this channel using various techniques to deceive users into taking harmful actions, often via misleading emails or websites. This paper explores the application of machine learning and deep learning techniques to enhance email security architecture, specifically in detecting phishing attacks. We present a supervised learning model trained on a comprehensive dataset encompassing multiple email attributes. The persistence of email phishing as a cybersecurity threat requires creative measures for timely detection and prevention. Therefore, the proposed email security system involves three layers of protection by checking the sender address, the email content, and the contained links. The trained machine learning model processes email content, extracts relevant features, and predicts the likelihood of phishing attempts. finally, the classification accuracy results of our machine-learning-based email security system are observed and evaluated.