![](https://csdnimg.cn/release/download_crawler_static/73212163/bg5.jpg)
FP-Radar: Longitudinal Measurement and Early Detection of Browser Fingerprinting 5
Fig. 2. FP-Radar: (a) We use the Wayback Machine to crawl historical snapshots of scripts present on Alexa top-100K websites from
2010 to 2019, (b) We first create AST representation of crawled scripts to extract web API keywords, we then model the temporal
co-occurrence of web APIs in scripts in a graph representation, (c) We leverages the temporal graph representation to predict future
co-occurrence of web API keywords, (d) We first leverage the predicted co-occurrence to cluster web APIs based on their functionality,
we then analyze the temporal clusters to detect abuse of specific API keywords for browser fingerprinting.
Wayback Machine crawl statistics. Despite the
aforementioned completeness issues in the Wayback Ma-
chine’s crawls, we are able to longitudinally crawl yearly
snapshots of almost 100K scripts from the Wayback Ma-
chine over the last decade (2010–2019). Based on clas-
sification of [103], this includes 1,658 fingerprinting and
92,193 non-fingerprinting scripts from our initial live
crawl. Note that we use a two step process to crawl the
Wayback Machine: we first fetch the URLs that point
to the historical snapshots of scripts [105] and then send
requests for those URLs to gather their script content.
The first step returns URLs with the timestamp and
the hash digest of the script content. The timestamps
enables us to crawl scripts that are one year apart from
each other and the hash digest helps us avoid crawling
duplicate scripts in the second step.
We acknowledge that FP-Radar’s longitudinal
data collection misses a substantial number of scripts
due to the completeness issues in the Wayback Ma-
chine. Specifically, with reference to our initial live crawl,
we note that FP-Radar is unable to crawl snapshots
of 43.09% of the scripts from the Wayback Machine.
While not ideal, we do not observe any bias in the miss-
ing scripts. Specifically, both fingerprinting and non-
fingerprinting scripts are missed with roughly the same
proportion, i.e., 43.60% and 46.74%, respectively. More-
over, despite the missing data, FP-Radar’s longitudi-
nal data collection is able to capture the overall trend
of increasing adoption of browser fingerprinting over the
years. Specifically, we observe fingerprinting scripts on
1.16% and 3.70% of the top-100K websites in 2016 and
2018, respectively. This corroborates with the findings of
prior studies of browser fingerprinting, which reported
that 1.43% of the top-million [25] and 3.69% of the top-
100K [17] websites conduct browser fingerprinting in
2016 and 2018, respectively. Thus, we conclude that FP-
Radar’s longitudinal data collection using the Wayback
Machine is sufficient for us to retrospectively study the
evolution of browser fingerprinting and draw meaningful
conclusions. We discuss alternates to the Wayback Ma-
chine and ideas to improve completeness of longitudinal
crawls in Section 5.
3.1.2 Graph Representation
To model guilt by association, we represent web API co-
occurrence in a graph. Specifically, we model API key-
words as nodes and include an edge between the nodes
if the API keywords co-occur in the same script. We
further weigh the edges based on the normalized co-
occurrence frequency.
API keyword extraction. To extract API keywords
from scripts, we model script text in abstract syn-
tax trees (ASTs) that normalize scripts for developer