Last week’s long PETS paper was very abstract; this week’s paper is very concrete. The authors are concerned that manually-curated blacklists, as currently used by most ad-blocking software, cannot hope to keep up with the churn in the online ad industry. (I saw a very similar talk at WPES back in 2012  which quoted the statistic that the default AdBlock Plus filter list contains 18,000 unique URLS, with new ones added at a rate of five to 15 every week.) They propose to train a machine classifier on network-level characteristics that differ between ad services and
They find that a set of five features provides a reasonably effective classification: proportion of requests that are
third-party (for transclusion into another website), number of unique referrers, ratio of received to sent bytes, and proportion of requests including cookies. For the training set they used, they achieve 83% precision and 85% recall, which is reasonable for a system that will be used to identify candidates for manual inspection and addition to blacklists.
There are several methodological bits in here which I liked. They use entropy-based discretization and information gain to identify valuable features and discard unhelpful ones. They compare a classifier trained on manually-labeled data (from a large HTTP traffic trace) with a classifier trained on the default AdBlock Plus filter list; both find similar features, but the ABP filter list has better coverage of infrequently used ads or analytics services, whereas the manually labeled training set catches a bunch of common ads and analytics services that ABP missed.
One fairly significant gap is that the training set is limited to cleartext HTTP. There’s a strong trend nowadays toward HTTPS for everything, including ads, but established ad providers are finding it difficult to cut all their services over efficiently, which might provide an opportunity for new providers—and thus cause a shift toward providers that have been missed by the blacklists.
There’s almost no discussion of false positives. Toward the end there is a brief mention of third-party services like Gravatar and Flattr, that share a usage pattern with ads/analytics and showed up as false positives. But it’s possible to enumerate common types of third-party services (other than ads and analytics) a priori: outsourced commenting (Disqus, hypothes.is), social media
share buttons (Facebook, Twitter), shared hosting of resources (jQuery, Google Fonts), static-content CDNs, etc. Probably, most of these are weeded out by the
ratio of received to sent bytes check, but I would still have liked to see an explicit check of at least a few of these.
And finally, nobody seems to have bothered talking to the people who actually maintain the ABP filter lists to find out how they do it. (I suspect it relies strongly on manual, informal reporting to a forum or something.) If this is to turn into anything more than an experiment, people need to be thinking about integration and operationalization.