emailspamfiltering

Email spam filtering is the process of identifying and removing unsolicited bulk email before it reaches a user's inbox or after receipt. It is implemented on mail servers, gateways, and sometimes client software to reduce unwanted messages and to protect users from phishing, malware, and scams.

Filtering techniques include content-based methods such as rule-based filters, keyword scoring, and statistical learning. Bayesian classifiers, support vector machines, neural networks, and other machine learning models analyze message features to estimate the probability of spam. Additional approaches rely on sender reputation, patterns of mass mailing, DNS-based blacklists, whitelists, greylisting, and attachment screening.

Filters may operate at different points in the email path: gateway or mail transfer agent level for servers, or on end-user devices. They often combine multiple signals and can be trained or configured with administrator-defined policies. User feedback and supervised learning help adapt filters to changing spam tactics.

Security and reliability are supported by complementary measures such as email authentication (SPF, DKIM, DMARC) to reduce spoofing, and user education about phishing. Open-source projects like SpamAssassin, as well as commercial cloud services, provide configurable filtering capabilities.

Evaluation uses metrics such as precision, recall, and false positive rate. Spam tactics evolve to evade detection, including obfuscated text, image-based messages, and malware-laden attachments, requiring ongoing updates and monitoring.