Investigating the effectiveness of web adblockers

by   Clayton Drazner, et al.
Rice University
Open Universiteit

We investigate adblocking filters and the extent to which websites and advertisers react when their content is impacted by these filters. We collected data daily from the Alexa Top-5000 web sites for 120 days, and from specific sites that newly appeared in filter lists for 140 days. By evaluating how long a filter rule triggers on a website, we can gauge how long it remains effective. We matched websites with both a regular adblocking filter list (EasyList) and with a specialized filter list that targets anti-adblocking logic (Nano Defender). From our data, we observe that the effectiveness of the EasyList adblocking filter decays a modest 0.13% per day, and after around 80 days seems to stabilize. We found no evidence for any significant decay in effectiveness of the more specialized, but less widely used, anti-adblocking removal filters.



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Adblocking, anti-adblocking, web crawling.

1. Introduction

Internet advertising is a significant source of income for many web sites as well as for apps on mobile platforms. On the other hand, advertisements are not desired by many users. Ads consume bandwidth and battery power, and are a source of attacks, such as malvertisements and social engineering hacks (e.g., “viruses found on your system!”). Adblockers help users block this unwanted content. Early adblockers were simple DNS blacklists. Modern adblockers follow a standardized syntax to specify patterns in hostnames as well as digging deeper into the DOM structure of a website to remove specific elements (see Section 2.2). A standardized syntax allows anyone to create filter rules. These rules can be generic to any web site while others are site-specific or even country-specific. EasyList and other organizations centrally publish lists of these rules, which can be imported by a variety of different ad-blocking extensions supported by most modern web browsers.

Such lists threaten advertising and the accompanying revenue. Unsurprisingly, both advertisers and their hosting websites have found a variety of ways to push back. Recent anti-adblocking technologies allow websites to detect the presence of adblocking systems and change their behavior accordingly. Websites can then request the user to disable their adblocker, or simply block the detected adblock-using visitor. This has the makings of an “adblock war”: users are blocking ads, and websites are blocking users. A new advertisement may initially bypass the filters, which will cause the filter lists to be updated and block the ad…which may cause the advertiser to update its code to defeat the blocking.

In this paper, we investigate whether filter lists and advertisers respond to each other in such a fashion. For three months, we continuously monitored filter lists and the websites affected by them, adding new websites as filter lists expanded. For each rule in a list, we tracked when it affected the corresponding website. We looked at the aggregated data to see if there was an overall trend, that is, how long it took for advertisers to react to a new blocking rule, and how long it took for filter rules to react to updated advertisements.

We will look at two separate filter lists: EasyList111, which is widely used and enabled by default in many browser ad-blocking plugins, and Nano Defender222, which is much less popular but specifically targets web page logic that attempts to defeat ad-blocking.

Contributions and limitations: When we began this work, no other study had used daily scans of large numbers of websites to analyze adblocking. Now, several other researchers have conducted studies in this area (discussed in Section 2.1), but with different methodologies.

Our study has several limitations. First, we use filter lists respecting the standard “Adblock Plus” syntax. We do not measure other adblocking or privacy-enhancing techniques, such as behaviors of or responses to browser extensions such as Ghostery or NoScript. Moreover, our study’s results are confined to the capabilities of the Adblock Plus engine and the coverage of the monitored lists: any advertisement not blockable by the engine, or not in any of the lists, is not considered. Lastly, there are several possible sources of noise in our data. Some of these are internal and known, such as failure to connect to a website on a specific day. There may also be external factors, such as a website switching ad providers.

2. Background Information

2.1. Related work

Many studies have analyzed online advertising and adblocking. Some examined overall adblock usage and/or sentiment, either by surveying users (Chanchary and Chiasson, 2015; Mathur et al., 2018) or analyzing aggregate user data (Malloy et al., 2016; Pujol et al., 2015). Others have looked at the effectiveness of adblocking as a privacy tool (e.g., (Bashir and Wilson, 2018; Fouad et al., 2018; Traverso et al., 2017; Wills and Uzunoglu, 2016)). A few have also examined topics such as browsing “quality of experience” (Newman and Bustamante, 2019) and performance (Garimella et al., 2017). Several studies took a more direct approach to detecting ads, such as automatically sourcing adblocker filter rules (Gugelmann et al., 2015), detecting ad-blockers through inspecting network traffic (Moro et al., 2018), foiling ad-blocker detection (Bruguera Micó, 2017), and perceptually detecting ads (Tramèr et al., 2018, 2019)).

The overarching adblock vs. advertiser arms race has also been studied. Storey et al. (Storey et al., 2017) characterize three stages in the arms race: first, adblocking by users versus ad obfuscation by advertisers; second, adblock detection by advertisers versus obfuscated adblocking by users; and last, blocking adblock detectors by users versus obfuscated adblock detectors by advertisers. Gritckevich et al. (Gritckevich et al., 2018)

examine adblockers from a game theory perspective, developing a model to examine how adblockers affect users and ad publishers. Mughees et al. 

(Mughees et al., 2016, 2017) use automated A/B testing to determine the incidence of anti-adblocking techniques on the web. They scanned the Alexa Top 100K and found at least 0.7% of sites use adblock-detection techniques, asking their users to turn off adblocking once detected. Nithyanand et al. (Nithyanand et al., 2016) study the prevalence of adblock detection techniques by focusing on third-party services in the Alexa Top 5K websites, finding that at least 6.7% of the sites in their sample used adblock detection techniques. Zhu et al. (Zhu et al., 2018) created an “anti-adblock” detection approach. They automatically visit targeted sites multiple times with and without adblocking and analyze the differences in the execution time. They found that 30.5% of the Alexa Top 5K used some form of anti-adblocking code.

Parts of the work by Iqbal et al. (Iqbal et al., 2017) resemble our study. In 2017 they set out to determine how adblock detection evolved. They approach this by using the Internet Archive to retrieve previous versions of websites and match them against a filter list. Our study uses similar adblock detection, but we collected live data directly, not archived data via a third party. In 2018, they published a followup study (Iqbal et al., 2018)

looking at multiple layers of the web stack (HTML, HTTP, Javascript), building a supervised machine learning model to block ads and trackers.

A recent tech report by Vastel et al. (Vastel et al., 2018) independently uses a similar approach to ours. They also analyzed EasyList’s performance on Alexa top sites in order to better understand filter lists. While doing so, they posed several of the same questions we do in this paper and used a number of methods that were similar to our own, albeit relying on a substantially different implementation approach. Our work on this area differs in two key ways from the work by Vastel et al. First, we examined more filter lists than just EasyList. Second, we use different statistical methods to summarize our data.

2.2. How adblocking works

Adblocking filters have rules expressed in a simple syntax333 Rules can simply specify a URL or domain name, with or without wildcards, which will prevent undesired elements from even being loaded. Rules can also specify DOM elements by ID or class, or by any of a variety of features, including a path through the DOM tree, or styling attributes like width and height. Such advertising elements will be removed from the DOM even if they’re added dynamically by JavaScript behaviors. Because these rules have the potential to be too broad, exception rules are supported, where an exception will override a blocking rule.

Anti-adblocking techniques generally work by introducing “bait” elements into the DOM that would be removed by an adblocker. By using JavaScript to inspect the DOM, any missing bait elements imply the presence of an adblocker. The site can then take additional actions, such as requesting the user to disable their adblocker. Bait elements can be avoided using exception rules. Alternatively, anti-anti-adblocking rules can directly target the JavaScript used by the anti-adblockers. EasyList has a specific policy with regard to anti-adblocking rules: “Anti-Adblock should only be challenged if the system limits website functionality or causes significant disruption to browsing”444, whereas Nano Defender has no such limitations.

3. Methodology

The goal of our experiment is to determine if and to what extent websites respond to adblock updates and adblockers respond to website updates. A meaningful answer to this question must be based on data collected from many websites and filter rulesets taken over time. Therefore, our methodology is described in two parts: data collection and data analysis.

3.1. Data collection

We collected two main types of data: daily iterations of the EasyList and Nano Defender (and predecessors) filter lists and daily scrapes of various targeted websites. For the filter lists, all the relevant files and commit histories are available on either GitHub or the AdblockPlus team’s Mercurial repository555 We iterated through every commit in the project histories for every particular filter list and then downloaded the final revision on every day there was at least one commit.

We note that Nano Defender effectively is a fork of an earlier project called Anti-Adblock Killer, apparently abandoned by its author in 2016. The Anti-Adblock Killer rules, inherited by Nano Defender, appear as a single large commit in the Nano Defender ruleset. As these rules are all significantly older than the time period of our web scraping activities we ignored them when looking specifically at the day-by-day impact of a given rule on our corpus of web scrapes.

Our approach to scraping websites differs from most studies discussed in Section 2.1. Generally speaking, most studies used Selenium to drive browsers running on local machines. Although doing so is highly effective for doing one-off jobs, we were performing daily scrapes on a large number of websites for months on end. We used the Scrapinghub cloud platform666

. Using their open-source Scrapy (configurable web scraper) and Splash (headless browser) libraries, we were able to easily scrape thousands of websites per day using one Scrapy cloud unit and a small Splash instance (which respectively cost $9 and $25 USD per month).

At every website we visited, we first scrolled to the bottom and waited 1.5 seconds, to allow for delayed behaviors. We saved a copy of the page’s DOM at this point as well as its HTTP Archive (HAR)777 for subsequent processing. During an approximately 140 day timespan, we collected roughly 487 GB of total data: close to 400 GB from the Alexa websites (collected during 120 days) and the remainder from websites specifically targeted by filter rules in Nano Defender list (collected during 140 days).

We scraped the Alexa Top 5K on a daily basis for 120 days, as well as websites targeted by the Anti-Adblock Killer and Nano Defender filter lists for 140 days. We used a Rake task888 that downloads any commits to the two filter lists on a daily basis and extracts the URLs from newly added rules. This ensures a short turnaround time between addition of a new filter rule for a new website, and that site’s inclusion in our scraper.

Our entire data collection process ran roughly from April through August in 2017 with a variety of false-starts and engineering challenges beforehand to get it running.

3.2. Data processing and analysis

For each web site image scraped, and for each set of filter rules against which we need to evaluate it, we used the open source Libadblockplus library999 to determine whether that day’s filter list would “trigger” on the downloaded version of a website for that day. We specifically chose Libadblockplus because it is a C++ wrapper around the Javascript Adblock Plus core engine, reducing the likelihood of its behavior differing from in-browser adblocking. Libadblockplus, when given the result of the web scraper and a filter list, returns a list of matched web page elements with their corresponding matched rules. An exception rule that also matches the same web page element will suppress that web page element from the list of results.

Recall that EasyList and Nano Defender use exception rules in different ways. EasyList uses exceptions to narrow otherwise overbroad positive rules, avoiding undesired damage to a web page. Nano Defender, on the other hand, uses exceptions to avoid touching bait elements used by anti-adblocking logic. As such, we invert the sense of Nano Defender’s exception rules; if a Nano Defender exception rule triggers on a web page element, we consider that to be a successful match, because it’s operating to defeat an anti-adblocker.

Due to the size of our data set, we split these jobs into smaller chunks (generally 250 websites at a time) which we ran in parallel on our institutional cluster. This enabled us to process months of scrape data using canonical versions of the various filter lists in a handful of days. We saved the results of these compute jobs as simple JSON files, in which we mapped websites to lists of (filter rule, offending resource) pairs for each day of scraped data.

We plotted these results (see e.g., Figure 2) with the time since the rule’s introduction on the horizontal axis and collected data on the vertical axis. Specifically, each row corresponds to one (website, filter-rule) tuple. For a given day and a given tuple (website, filter-rule), the value of the point in the graph is either true, false, or fail, and colored as follows:

  • Black: true—the archived copy for day of the website triggered a hit on the filter rule.

  • White: false—the archived copy for day did not trigger a hit.

  • Gray: fail—no data available.

In our graphs and data processing, rows with filter rules that never triggered on those sites are omitted. As such, for each combination of a website and a filter rule that triggered at least once on that website, we ended up with a row of data in our graphs. Failures may be due to failure to contact the website, but also occur when the day is outside the observation window of the website. We discuss this further, below.

Our graphing technique is designed to align each row based on the date at which the rule was introduced. Each value of shows whether filter rule still triggered on the copy of website after days.

To clarify this alignment process, consider Figure 2, where there are two gray “triangles” of missing data. The data rows adjacent to the lower-left triangle correspond to cases where the rule predates the start of our experiment. So if a rule was 80 days old at the start of the experiment, then we would only render results for . The data rows adjacent to the upper-right triangle correspond to cases where the rule appeared while our experiment was ongoing. So, if a rule appeared on day 80, we would only have 40 days of results for the effectiveness of that rule, appearing at .

A consequence of this alignment process is that vertical slices through the graph contain all the filtering effectiveness data we have for rules of a given age.

3.3. Statistical Methods

Plotting the data as described above allows us to gauge the effectiveness of filter list rules over time. Specifically, we use the ratio of total number of hits for a given day versus the total number of hits misses as an approximation of the overall effectiveness of filter list rules after days. We would expect this ratio to decline as websites take measures to reduce the effectiveness of filter rules.

Note that collapsing an entire column into a single value misses out on sources of uncertainty hidden in the value. The process of scraping websites includes a number of sources of noise. Principal amongst these is that the number of filter rules for which we do not have data varies from day to day. For example, in Figure 4, there is little data available after 40 days, and scant after 80. Thus, it is essential to model the uncertainty in our data.

To get meaningful error bars for our graphs, we cannot simply take the standard deviation of a sequence of 1’s and 0’s. Instead, we use

bootstrap resampling (see, e.g., (Pezzullo, accessed November 29, 2018)

), a standard technique for computing many common statistics. In our case, we compute the 95% confidence interval over the mean. Bootstrap resampling produces a robust result without requiring the data to be normally distributed 

(Desharnais et al., 2015).

Figure 1. EasyList effectiveness on Alexa Top 5K (raw data, rasterized)
Figure 2. EasyList effectiveness on Alexa Top 5K (average with confidence intervals)
Figure 1. EasyList effectiveness on Alexa Top 5K (raw data, rasterized)

Bootstrap resampling in a nutshell: Bootstrap resampling is a statistical measure that relies on sampling with replacement. Consider a data set with elements over which the average is computed. To determine the confidence intervals, we first randomly sample (with replacement) values from the data set. We compute the average of this sample. We then repeat this sampling process 5,000 times, yielding 5,000 so-called “resampled averages”. These are then sorted from small to large. The 2.5th percentile and the 97.5th percentile of this list then provide us with bounds on the confidence interval. This corresponds to a 95% confidence interval around the average. We then can render these confidence intervals as error bars on our graphs.

4. Analysis

We first consider the popular EasyList filter and next look at the more targeted Nano Defender filters. We also consider how our data can confirm results from prior studies.

4.1. EasyList

Our analysis of the effectiveness of EasyList filter rules showed two discontinuous sections (see Figure 2). This is due to the denominator changing significantly after , which is an artifact of our methodology. As can be seen in Figure 2, we have significantly more data for the first 75 days of filter rules existence than for the latter 45 days. This is because EasyList added a large number of filter rules for new websites on day 45 of our experiment. Since we could monitor the performance of these rules from their introduction, these are all plotted from in Figure 2.

In Figure 2, the first section depicts a long period in which large number of EasyList’s filter rules show declining effectiveness. A simple linear best fit of this section (plotted as a straight line on the graph) shows a decrease of just over 0.13% per day, with a total loss of roughly 10% in about 75 days. These results exclude EasyList’s “exception rules” (see Section 2.2). Taking these rules into account, we see a the decrease of roughly 0.2% per day on average, with a total loss of roughly 15% in the same period of time. (Visually, the resulting graphs appear similar to Figure 2 and are omitted for space.) The obvious explanation is that websites and advertisers do indeed respond to the introduction of new EasyList filter rules.

Following the discontinuity at approximately day 75 we observe no significant decline; attempting to plot a linear best fit as we did in the prior section of the graph results in a horizontal line. This suggests the underlying process of website operators and advertisers responding to EasyList is not a linear process. A longer time period of data collection over many more websites might well show a fit to an exponential curve.

Figure 3. Nano Defender effectiveness on the Alexa Top 5K (raw data)
Figure 4. Nano Defender effectiveness on the Alexa Top 5K (average with confidence intervals)
Figure 3. Nano Defender effectiveness on the Alexa Top 5K (raw data)
Figure 5. Nano Defender effectiveness on websites targeted by Nano Defender

4.2. Nano Defender

We next examine the effectiveness of the more targeted anti-adblock filter rules in the Nano Defender filter list. In addition to the Alexa Top 5K (see Figure 4) we also looked at websites outside this list that were specifically targeted by Nano Defender (see Figure 5).

Figure 4 shows that initially the filter effectiveness rate is approximated by a horizontal line. The upward trend in the averages is overwhelmed by the growing confidence intervals. Our best interpretation is that there is no evidence of a decay in Nano Defender’s filter rule effectiveness over time.

From roughly on, we no longer have sufficient data. This is clearly illustrated in the increasing size of the confidence intervals, and also apparent in Figure 4. Notwithstanding this lack of data for later days, the absence of a downward trend in the first part of Figure 4 is significant, and thus we conclude that in general, websites are currently neither tracking nor responding to updates in the Nano Defender filter rules.

A curious possibility is that the absence of observed filter effectiveness decay in the Nano Defender data, versus the presence of decay in the EasyList data, controls for the possibility that web sites are simply drifting over time in their engineering practices. If that were the cause of our observed filter effectiveness decay, then we should see a similar effect in both data sets. Contrarily, the engineering of Nano Defender is much more specifically targeted than the engineering of EasyList, so it’s also possible that a “drift effect” would impact EasyList’s effectiveness more than it impacts Nano Defender.

4.3. Comparing results with prior studies

Iqbal et al. (Iqbal et al., 2017) also looked at subsections of EasyList and Anti-Adblock Killer (their work predates the Nano Defender list). They found, for the Alexa Top 5K websites, that Nano triggered on 8.7% websites and that AdblockWarningRemovalList combined with the anti-adblock sections of EasyList (hereafter “AWRL/EasyList” ) only triggered on 0.4% websites. Nithyanand et al. (Nithyanand et al., 2016) similarly found anti-adblocking logic on 6.7% of the Alexa Top 5K.

To compare their results with our data, where we have multiple samples of each filter lists and of each website, we restate their question as follows: Do any versions of Nano Defender list or AWRL/EasyList trigger on any versions of each given website? We find that the combined Nano Defender list triggered on roughly 13.3% of the Alexa Top 5K (exactly 666 unique websites) and that AWRL/EasyList triggered on 0.06% of the Alexa Top 5K (exactly 3 websites). Our observed growth in anti-adblocking logic relative to Iqbal’s results likely combines two effects: a genuine increase in websites using such logic, as well as increased engineering efforts on the part of Nano Defender to detect and filter such logic. AWRL/EasyList has remained comparatively static with fairly few commits in the same period of time; websites and advertisers have clearly engineered around AWRL/EasyList.

We next focus on PageFair, a commercial service that provides websites with adblocking analytics and adblock-resistant advertising. Nithyanand et al. (Nithyanand et al., 2016) looked at a number of such services, finding 20 web sites using PageFair, which was then successfully blocked by AdBlockPlus and Privacy Badger, but not Ghostery. We detected 67 separate websites in the Alexa Top 5K using PageFair, all of which are successfully filtered by the Nano Defender list.

Nithyanand also discussed the arms race of websites detecting and responding to adblockers. They noted that adblock detection scripts are often loaded from popular content distribution networks such as Cloudflare. One prominent such project is “BlockAdblock”. This project appears to have been available from Cloudflare since at least August 2015. We detected 20 websites in the Alexa Top 5K using some variant of BlockAdblock. Surprisingly, we detected only one of these websites using the suggested Cloudflare CDN URL; 8 of the remaining 19 used a URL owned by an advertising company, another 10 chose to serve a copy of the script themselves, and one website used a different CDN service.

5. Conclusions and future work

We observed an approximate 0.13% decrease per day in the effectiveness on the Alexa Top 5000 web sites of new rules in the EasyList filter list in the immediate period after they were added. However, websites did not appear to be actively responding to updates in the more specialized and less widely used Nano Defender list.

There are numerous opportunities for additional work in this area. For example, scaling up our methodology to run on millions rather than thousands of web sites, and for longer periods of time, would certainly be feasible and interesting. Using Scrapinghub, scaling the data collection is straightforward, albeit more expensive. Analyzing larger volumes of collected data would certainly require larger computing clusters, which require an additional expense to rent. At least the process is straightforward to distribute on a cluster, since each web site scrape, evaluated against each filter ruleset, is a completely independent task.

Another interesting possibility would be to cluster the various anti-adblocking mechanisms that a longer-term survey might discover over time, to understand the diversity of the anti-adblocking ecosystem.


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