What are machine learning spam filters?

ML spam filters use algorithms trained on large datasets to identify spam patterns. Unlike rule-based systems, they learn complex patterns automatically and adapt as spam evolves. Most modern filters incorporate ML.

Techniques include: neural networks recognizing content patterns, clustering identifying campaign similarities, and reinforcement learning improving from user feedback. Multiple algorithms often work together.

Advantages: handle complex patterns humans can't specify, adapt continuously, and generalize to new variations. Challenges: require substantial training data, can be manipulated by adversarial inputs, and decisions can be difficult to explain.