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Anomalies in emails such as phishing and spam present major security riskssuch as the loss of privacy, money, and brand reputation to both individualsand organizations. Previous studies on email anomaly detection relied on asingle 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 languageof the email content. To overcome this deficit, this study conducted featureextraction and selection on email header datasets and leveraged both multi andone-class anomaly detection approaches. Experimental analysis results obtaineddemonstrate that email header information only is enough to reliably detectspam and phishing emails. Supervised learning algorithms such as Random Forest,SVM, MLP, KNN, and their stacked ensembles were found to be very successful,achieving high accuracy scores of 97% for phishing and 99% for spam emails.One-class classification with One-Class SVM achieved accuracy scores of 87% and89% with spam and phishing emails, respectively. Real-world email filteringapplications will benefit from the use of only the header information in termsof resources utilization and efficiency.

Anomaly Detection in Emails using Machine Learning and Header Information