emailfiltering

Email filtering is the process of inspecting email messages to identify and separate unwanted content from legitimate mail. It is used to reduce spam, protect users from phishing and malware, and enforce organizational policies.

Filters use multiple signals: content analysis (keywords, heuristics, Bayesian scoring), metadata (sender reputation, headers), and external databases (blacklists and allowlists). Modern systems combine rule-based filters with machine learning models that rank messages by likelihood of unwanted content. Protocols such as DMARC, DKIM, and SPF help verify sender identity and reduce spoofing.

Common types include inbound spam filtering, phishing and malware detection, attachment scanning, and outbound filtering for data loss prevention and compliance.

Deployment options include on-premises gateways, cloud-based services, and client-side plugins. Filters may operate at the mail transfer agent level, mailbox level, or end-user device, and many environments use layered filtering with several stages.

Evaluation and challenges: performance is assessed by precision, recall, F1 score, and ROC curves. High precision minimizes false positives; high recall minimizes missed threats. Spammers adapt tactics, requiring updates and occasional retraining. Privacy concerns, over- or under-filtering, and regulatory compliance are ongoing considerations.

History: early approaches used basic keywords and rules; the 1990s saw Bayesian filtering, followed by data-driven machine learning. Today, email filtering is widely deployed in open-source projects and commercial products, installed at gateways, mail servers, or in the cloud.