Measuring Internet Censorship

Norwegian writer Mette Newth once wrote that: “censorship has followed the free expressions of men and women like a shadow throughout history.” Indeed, as we develop innovative and more effective tools to gather and create information, new means to control, erase and censor that information evolve alongside it. But how do we study Internet censorship?

Organisations such as Reporters Without Borders, Freedom House, or the Open Net Initiative periodically report on the extent of censorship worldwide. But as countries that are fond of censorship are not particularly keen to share details, we must resort to probing filtered networks, i.e., generating requests from within them to see what gets blocked and what gets through. We cannot hope to record all the possible censorship-triggering events, so our understanding of what is or isn’t acceptable to the censor will only ever be partial. And of course it’s risky, or even outright illegal, to probe the censor’s limits within countries with strict censorship and surveillance programs.

This is why the leak of 600GB of logs from hardware appliances used to filter internet traffic in and out of Syria was a unique opportunity to examine the workings of a real-world internet censorship apparatus.

Leaked by the hacktivist group Telecomix, the logs cover a period of nine days in 2011, drawn from seven Blue Coat SG-9000 internet proxies. The sale of equipment like this to countries such as Syria is banned by the US and EU. California-based manufacturer Blue Coat Systems denied making the sales but confirmed the authenticity of the logs – and Dubai-based firm Computerlinks FZCO later settled on a US$2.8m fine for unlawful export. In 2013, researchers at the University of Toronto’s Citizen Lab demonstrated how authoritarian regimes in Saudi Arabia, UAE, Qatar, Yemen, Egypt and Kuwait all rely on US-made equipment like those from Blue Coat or McAfee’s SmartFilter software to perform filtering.

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Understanding Online Dating Scams

Our research on online dating scams will be presented at the  Conference on Detection of Intrusions and Malware and Vulnerability Assessment (DIMVA) that will be held in Milan in July. This work was a collaboration with colleagues working for Jiayuan, the largest online dating site in China, and is the first large-scale measurement of online dating scams, comprising a dataset of more than 500k accounts used by scammers on Jiayuan across 2012 and 2013.

As someone who has spent a considerable amount of time researching ways to mitigate malicious activity on online services, online dating scams picked my interest for a number of reasons. First, online dating sites operate following completely different dynamics compared to traditional online social networks. On a regular social network (say Facebook or Linkedin) users connect with people they know in real life, and any request to connect from an unknown person is considered unsolicited and potentially malicious. Many malicious content detection systems (including my own) leverage this observation to detect malicious accounts. Putting people who don’t know each other in contact, however, is the main purpose of online dating sites – for this reason, traditional methods to detect fake and malevolent accounts cannot be applied to this context, and the development of a new threat model is required. As a second differentiator, online dating users tend to use the site only for the first contact, and move to other media (text messages, instant messaging) after that. Although that is fine for regular use, it makes it more difficult to track scammers, because the online dating site loses visibility of the messages exchanged between users after they have left the site. Third, online dating scams have a strong human component, which differentiates them heavily from traditional malicious activity on online services such as spam, phishing, or malware.

We identified three types of scams happening on Jiayuan. The first one involves advertising of  escort services or illicit goods, and is very similar to traditional spam. The other two are far more interesting and specific to the online dating landscape. One type of scammers are what we call swindlers. For this scheme, the scammer starts a long-distance relationship with an emotionally vulnerable victim, and eventually asks her for money, for example to purchase the flight ticket to visit her. Needless to say, after the money has been transferred the scammer disappears. Another interesting type of scams that we identified are what we call dates for profit. In this scheme, attractive young ladies are hired by the owners of fancy restaurants. The scam then consists in having the ladies contact people on the dating site, taking them on a date at the restaurant, having the victim pay for the meal, and never arranging a second date. This scam is particularly interesting, because there are good chances that the victim will never realize that he’s been scammed – in fact, he probably had a good time.

In the paper we analyze the accounts that we detected belonging to the different scam types, and extract typical information about the demographics that scammers pose as in their accounts, as well as the demographics of their victims. For example, we show that swindlers usually pose as widowed mid-aged men and target widowed women. We then analyze the modus operandi of scam accounts, showing that specific types of scam accounts have a higher chance of getting the attention of their victims and receiving replies than regular users. Finally, we show that the activity performed on the site by scammers is mostly manual, and that the use of infected computers and botnet to spread content – which is prominent on other online services – is minimal.

We believe that the observations provided in this paper will shed some light on a so far understudied problem in the field of computer security, and will help researchers in developing systems that can automatically detect such scam accounts and block them before they have a chance to reach their victims.

The full paper is available on my website.

Update (2015-05-15): There is press coverage of this paper in Schneier on Security and BuzzFeed.

Teaching cybersecurity to criminologists

I recently had the pleasure of teaching my first module at UCL, an introduction to cybersecurity for students in the SECReT doctoral training centre.

The module had been taught before, but always from a fairly computer-science-heavy perspective. Given that the students had largely no background in computer science, and that my joint appointment in the Department of Security and Crime Science has given me at least some small insight into what aspects of cybersecurity criminologists might find interesting, I chose to design the lecture material largely from scratch. I tried to balance the technical components of cybersecurity that I felt everyone needed to know (which, perhaps unsurprisingly, included a fair amount of cryptography) with high-level design principles and the overarching question of how we define security. Although I say I designed the curriculum from scratch, I of course ended up borrowing heavily from others, most notably from the lecture and exam material of my former supervisor’s undergraduate cybersecurity module (thanks, Stefan!) and from George’s lecture material for Introduction to Computer Security. If anyone’s curious, the lecture material is available on my website.

As I said, the students in the Crime Science department (and in particular the ones taking this module) had little to no background in computer science.  Instead, they had a diverse set of academic backgrounds: psychology, political science, forensics, etc. One of the students’ proposed dissertation titles was “Using gold nanoparticles on metal oxide semiconducting gas sensors to increase sensitivity when detecting illicit materials, such as explosives,” so it’s an understatement to say that we were approaching cybersecurity from different directions!

With that in mind, one of the first things I did in my first lecture was to take a poll on who was familiar with certain concepts (e.g., SSH, malware, the structure of the Internet), and what people were interested in learning about (e.g., digital forensics, cryptanalysis, anonymity). I don’t know what I was expecting, but the responses really blew me away! The students overwhelmingly wanted to hear about how to secure themselves on the Internet, both in terms of personal security habits (e.g., using browser extensions) and in terms of understanding what and how things might go wrong. Almost the whole class specifically requested Tor, and a few had even used it before.

This theme of being (pleasantly!) surprised continued throughout the term.  When I taught certificates, the students asked not for more details on how they work, but if there was a body responsible for governing certificate authorities and if it was possible to sue them if they misbehave. When I taught authentication, we played a Scattergories-style game to weigh the pros and cons of various authentication mechanisms, and they came up with answers like “a con of backup security questions is that they reveal cultural trends that may then be used to reveal age, ethnicity, gender, etc.”

There’s still a month and a half left until the students take the exam, so it’s too soon to say how effective it was at teaching them cybersecurity, but for me the experience was a clear success and one that I look forward to repeating and refining in the future.