  Website Fingerprinting (WF) attacks identify the websites visited by users by
performing traffic analysis, compromising user privacy. Particularly, DL-based
WF attacks demonstrate impressive attack performance. However, the
effectiveness of DL-based WF attacks relies on the collected complete and pure
traffic during the page loading, which impacts the practicality of these
attacks. The WF performance is rather low under dynamic network conditions and
various WF defenses, particularly when the analyzed traffic is only a small
part of the complete traffic. In this paper, we propose Holmes, a robust and
reliable early-stage WF attack. Holmes utilizes temporal and spatial
distribution analysis of website traffic to effectively identify websites in
the early stages of page loading. Specifically, Holmes develops adaptive data
augmentation based on the temporal distribution of website traffic and utilizes
a supervised contrastive learning method to extract the correlations between
the early-stage traffic and the pre-collected complete traffic. Holmes
accurately identifies traffic in the early stages of page loading by computing
the correlation of the traffic with the spatial distribution information, which
ensures robust and reliable detection according to early-stage traffic. We
extensively evaluate Holmes using six datasets. Compared to nine existing
DL-based WF attacks, Holmes improves the F1-score of identifying early-stage
traffic by an average of 169.18%. Furthermore, we replay the traffic of
visiting real-world dark web websites. Holmes successfully identifies dark web
websites when the ratio of page loading on average is only 21.71%, with an
average precision improvement of 169.36% over the existing WF attacks.
