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Lies, Damn Lies and Spam Statistics

The Internet is a fiendishly difficult thing to measure, but that doesn’t prevent corporations and analysts from pretending to be able to do it with a level of precision that is completely unjustified by the quality of their data. This is easy to spot if you compare how different companies report the same thing. For example, in the latest monthly data from Compete.com, Yahoo! is listed as the second most popular web site in the US, with 164,326,984 unique visitors.

Compete.com top ten

Wow, they have that accurate to nine significant figures, that’s impressive. But now let’s take a look at Quantcast.com’s data. In their latest monthly figures Yahoo! is listed as the ninth most popular web site with 69,470,416 unique visitors.

Quantcast top ten

OK, how about Comscore.com? They also have Yahoo! at number two, but with 192,851,000 unique visitors.

Comscore data

At least Comscore rounds to the nearest thousand, and their listing says “Yahoo! Sites” so they may be including Flickr.com as well. Leaving aside Comscore, which may not be measuring the same thing, the other numbers (both for yahoo.com) differ by a factor of almost 2.4. They can’t both be right. Even if you round to the nearest hundred million, they are still different. Alexa.com, incidentally, currently lists Yahoo! in fourth place, but does not give absolute numbers for visitors, merely a “Percentage Reach” figure to two significant figures. Thank you Alexa for not claiming more accuracy than your measurement techniques can possibly justify.

We see the same thing in statistics relating to spam. Every so often one or another messaging security company will announce that 92.76% of all email is spam (down 1.37% from last month) or that the largest source of spam in the world is now Roubaix, France, and that is reported in the press as gospel truth. I’ll take a look at our statistics and quite often see something completely different. Why is that? I think there are two main reasons. The first is that different companies have different client bases, introducing sample bias. The second is that many large organizations have more than one spam filtering process in place, so the quantity and quality of spam seen by a particular filter depends on where it is in the chain of filters for that installation.

Cloudmark receives spam reports from a wide variety of sources, including our Cloudmark Security Platform clients in sixty four countries and from Cloudmark Desktop users world wide. We believe we have a clearer and more complete picture of world spam than any other company, but there may still be sample bias in our data, and we are certain that there is sample bias in companies who do not have as great a global reach.

Most major ISPs use more than one type of spam filtering. IP address blacklisting is a simple and fast method of spam filtering. It does not stop all spam, but it is a good first line of defense. With Cloudmark Security Platform for Email or similar mail server software, blacklisted IP addresses are not even allowed to connect to the mail server. In that case, there is no way of saying how many spam messages are being blocked, nor how many recipients those messages would have been sent to, because the messages themselves, do not make is as far as the mail server, let alone the user’s spam folder.

Incidentally, that’s one reason why Cloudmark’s quarterly messaging threat reports discuss the number and percentage of blacklisted IP addresses by country as a measure of the spam threats from that country, rather than trying to measure spam volumes.

After the IP address blocking the next step is policy based filtering. This may impose rate limits on IP address blocks, or other filtering or flagging based on the email’s metadata. As IPv6 rolls out, and the IP address space is vastly expanded, policy based filtering and rate limiting will become far more important, as traditional IP blacklisting, even at the network level, will be exponentially more difficult. However, each ISP may set different policy rules. A burst of email that may sail through one one provider’s filters may be slowed to a crawl or stopped entirely by another. While it’s possible to collect statistics from policy filtering, statistics from different ISPs will not be directly comparable.

The last line of defense is content based filtering. This takes what is left after IP blacklists and policy have been applied, and decides if it goes in the inbox or the spam folder. This is the spam that is the most difficult to filter, as the more sophisticated spammers are continually changing their message content and calls to action, so that any fingerprinting system must respond equally quickly to keep up with them. This is cyber guerrilla warfare and it is vicious and incessant. The fact that you hardly ever see spam in your in box is not because we have won the war on spam, it is because we keep winning all the battles.

It’s this final stage of filtering that many of the statistics quoted in this blog come from. It’s the news from the last line of defense against the spammers – an automated system that can respond to new threats within seconds backed by 7/24 human monitoring from our Security Operations Center and Accuracy Engineers. If our statistics are different from other companies’ statistics, I think it’s because we are focusing on the most serious threats.

UPDATE August 12, 2013: Kudos to UPI for reporting on a finding from Kaspersky Lab’s spam statistics, and pointing out that Spamhaus gets a completely different result.


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