The application provides a website fingerprinting
attack method, device and equipment, which maps original traffic trajectory to a structured
feature matrix through a bilateral feature representation module, divides a standardized timeline into fixed windows, extracts scale-invariant representation through a multi-scale feature migration module to
process scale changes across browsers in the traffic trajectory, pre-trains the module using reference traffic data, learns traffic patterns of each website under
low delay, obtains a pre-training model, performs few-shot training on the pre-training model using limited and truncated Tor traffic, realizes effective cross-
domain knowledge migration, generates a final
feature vector, and uses a classifier to classify the
feature vector, which significantly improves the analysis and identification performance of anonymous communication traffic under the condition of few samples and incomplete sample trajectories, and provides systematic technical solutions and methodological support for
traffic analysis of the Tor anonymous
system in actual network environments.