New classes in seconds, without retraining
A research note from the Adaptive AI Lab — classification by retrieval adds new encrypted-traffic classes without retraining, and detects unknown traffic in the same step.
Research note · September 2026 · Encrypted Traffic
Most traffic classifiers become expensive to maintain when applications and threats change: every new application means collecting data, retraining, and redeploying the model. Classification by retrieval takes a different path — it stores compact flow representations and learns a new class by adding a few labeled examples to the index. No retraining run, no downtime.
The same nearest-neighbor distance that assigns a class also tells us when traffic belongs to none of the known classes, so out-of-distribution detection comes from the same retrieval step — a practical answer to zero-day and previously unseen traffic.
Read the Computer Networks paper → · Explore the encrypted-traffic project →