Eight years ago, in 2003, we proposed and presented the use of linguistic analysis for email author identification. Our use case was started with the investigation of Advanced Fee Fraud (AFF), also known as 419 scams from Nigeria. We proved, albeit from a small data set, that language can identify a message author using several key indicators. We further proved that bias made victims far more susceptible to social engineering attacks.

About five years later, in 2008, an educational institution in Quebec picked up this theme of email author identification by applying pattern analysis to data sets. They released an online paper called A novel approach of mining write-prints for authorship attribution in e-mail forensics

In this paper, we introduce an innovative data mining method to capture the write-print of every suspect and model it as combinations of features that occurred frequently in the suspect’s e-mails. This notion is called frequent pattern, which has proven to be effective in many data mining applications, but it is the first time to be applied to the problem of authorship attribution.

Er, well, they are obviously wrong. The first time was not 2008. It probably was not even in 2006 (when we wrote our paper) or 2003. I would be far more impressed if they gave a little credit to the long history of language and data analysis, let alone our published and presented work. Our presentations on pattern frequency for authorship attribution predates not only their paper but, for at least two or three of the authors, their entire career.

At the start of 2010 we presented our findings at the RSA Conference in San Francisco and showed how anonymous authors could be distinguished using linguistic analysis. We pulled apart email messages, presented them based on their use of language (including stylometric features), and presented a taxonomy that predicts fraud based on key indicators.

The audience in our presentations always gets a quiz at the end; many always seem surprised they suddenly are able to see uniqueness in messages where none existed prior.

I just noticed that the Quebec crew have republished their paper under a more contemporary title with almost the same specific use case in mind: Mining writeprints from anonymous e-mails for forensic investigation

In this paper, we focus on the problem of mining the writing styles from a collection of e-mails written by multiple anonymous authors. The general idea is to first cluster the anonymous e-mail by the stylometric features and then extract the writeprint, i.e., the unique writing style, from each cluster. We emphasize that the presented problem together with our proposed solution is different from the traditional problem of authorship identification, which assumes training data is available for building a classifier.

Here is a major differentiation point. We did not assume a massive amount of training data was available or necessary to build a classifier. Our system can be taught to virtually anyone so that they then can start identifying authorship immediately. We have applied it and presented around the world, from Turkey to Brazil, with success.

Here is another major differentiation point. We were not trying to beg “first time” innovation recognition because we combined the extant body of knowledge in linguistics and security (social engineering). It was done in a novel way to help reduce fraud — stop people from falling victim to 419 scams — but we gave attribution.

We could have saved them a lot of time and hassle since we have been reporting it for eight years now. Perhaps there is a chance for collaboration in the future.

I could go on with differentiation points, but here’s one more. We don’t charge you to read our paper or presentation.

I mean Pi. Funny example of security control failure:

It seems these scientists want to ward off ruffians who can’t do advanced math. After all, the service they’re offering is access to truly random numbers — a difficult computer science feat on its own, and one that only responsible adults should have access to.

The scientists thought it would be a good idea to give their viewers a math challenge — solve a basic calculus problem to prove they are human. An equation version of the CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is posted on their sign up page. Would this be called a CAPECHA?

But these math elitists may have a problem on their hands. As calculus teachers around the world are now discovering, the Internet will now do your math homework for you. Just go to WolframAlpha and pop in the problem, and boom, you’ll have access to all the random numbers your heart could desire.

Perhaps they did it for the publicity, or just for the humor. Or maybe they did it to drive up the market price for hired CAPTCHA-solution labor. I wonder if the next Craigslist ad for “easy money, work from home” will include basic calculus as a required skill.

The CEO of Sonic.net, a broadband provider, has a blog post with some interesting details. Here is his argument for why the US has such slow broadband.

In 1996, the US Congress kicked off the broadband revolution when it passed the Telecom Act. The 1996 Act created a level playing field for competitive carriers, and brought about widespread deployment of DSL and other broadband technologies.

Then in 2003 and 2004, the then Republican led FCC reversed course, removing shared access to essential fiber infrastructure for competitive carriers and codifying instead a policy of exclusive use and “multi-modal competition”.

[…]

Elsewhere in the world, regulatory bodies followed the lead of the US Congress and separated essential copper and fiber infrastructure from the services and providers who used them, and the result has been amazing. In Asia and Europe, Gigabit services are becoming common, and the price paid by consumers per megabit is a tiny fraction of what we pay here at home.

The bottom-line seems to be a failure of politicians to fight for better management of shared (collective) resources. The US needs a national broadband policy that aggressively promotes true competition, based upon the separation of retail network services and wholesale network transport. Greater freedom and innovation clearly can come from shared roads, shared electric lines, shared stop-lights, shared fire-hydrants…why not shared fiber?

We must build new fiber all the way to your home, passing by along the way the idle fiber infrastructure that the FCC set aside nearly a decade ago.

The American government and phone companies in 1992 said they were working to put fiber to homes.

The phone companies painted a bright picture of the wonders of fiber optics and the Information Age — the latest movies available at the flick of a remote control, the Library of Congress via a personal computer and picture phones out of “2001: A Space Odyssey.

It was a good start under President Clinton, but serious impediments stood in the way. The Brookings Institute in 2002 tried to get the Bush Administration to turn up the heat and put the focus on improving broadband speeds:

The principal source of the problem is monopolistic structure, entrenched management, and political power of the ILEC and CATV sectors, worsened by major deficiencies in the policy and regulatory systems covering these industries.

The Sonic.net CEO explained above how that all turned out, as the US watches the world pass it by. One might think the following sentence would have received more attention, even from a President busy starting two wars:

Failure to improve broadband performance could reduce U.S productivity growth by 1% per year or more, as well as reducing public safety, military preparedness, and energy security.

Alas, while the US rapidly increased domestic broadband subscribers in 2001 to 2009 from 9% to 63.5% it actually has been in decline relative to the rest of the world. Today it does not even make the top ten — behind fifteen or more other countries.

Even European Mobile Broadband Penetration (e.g. smart phones) is twice that of the Americas.