Phishing email header datasets contain various features that help identify phishing attempts, such as the sender’s email address, recipient’s address, subject line, and the email's routing information (e.g., SMTP servers, return paths). They also include technical headers like Message-ID, authentication results (SPF, DKIM, DMARC), content type, and spam scores, which can reveal suspicious patterns or mismatched information. Anomalies in these features—such as unusual sender addresses, failed authentication checks, or malicious URLs—serve as indicators of phishing emails. By analyzing these attributes, machine learning models and rule-based systems can detect phishing threats effectively.