Anomaly Detection in Emails using Machine Learning and Header Information

Understand

Anomalies in emails such as phishing and spam present major security risks such as the loss of privacy, money, and brand reputation to both individuals and organizations.

• Previous studies on email anomaly detection relied on a single type of anomaly and the analysis of the email body and subject content.
• A drawback of this approach is that it takes into account the written language of the email content.
• To overcome this deficit, this study conducted feature extraction and selection on email header datasets and leveraged both multi and one-class anomaly detection approaches.