Antispam

Antispam refers to a set of techniques and policies used to detect, filter, and block unsolicited bulk messages, most commonly email, but also text messaging, social media, and web forms. The goal is to reduce unwanted communications, protect users from scams and malware, and preserve network bandwidth and system resources.

Origins trace back to early Internet spam in the 1990s, with initial measures such as blacklists of known spammers and simple keyword filters. Over time, more sophisticated methods emerged, including content-based filtering, sender reputation, and authentication protocols to verify legitimate senders.

Techniques used in antispam include content-based filtering (such as Bayesian classifiers and other machine learning approaches), metadata and header analysis, and reputation systems for IPs and domains. Rules-based scoring and heuristics are often combined with machine learning signals. Sender verification technologies like SPF, DKIM, and DMARC help detect spoofing, while blocklists, allowlists, URL reputation, and image analysis address obfuscated content and phishing. Anti-spam systems may also employ OCR for image-based spam and link analysis to detect malicious destinations.

Deployment models vary from server-side filtering at mail gateways or cloud-based services to client-side filters integrated into email clients or apps. Effective antispam often includes user education and clear opt-out mechanisms, along with collaboration between providers and administrators.

Common targets include email spam, comment spam, forum spam, SMS spam, and social media messages. Phishing, malware distribution, and business email compromise remain prominent risks; trends include evolving tactics and encrypted traffic that challenge detection.

Legal frameworks and policy considerations, such as the CAN-SPAM Act in the United States and GDPR-related data handling requirements in the EU, shape how antispam measures are implemented and enforced. Evaluation metrics typically cover precision, recall, and F1, with attention to minimizing false positives and negatives.