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.

Banks undermine chip and PIN security because they see profits rise faster than fraud

The Chip and PIN card payment system has been mandatory in the UK since 2006, but only now is it being slowly introduced in the US. In western Europe more than 96% of card transactions in the last quarter of 2014 used chipped credit or debit cards, compared to just 0.03% in the US.

Yet at the same time, in the UK and elsewhere a new generation of Chip and PIN cards have arrived that allow contactless payments – transactions that don’t require a PIN code. Why would card issuers offer a means to circumvent the security Chip and PIN offers?

Chip and Problems

Chip and PIN is supposed to reduce two main types of fraud. Counterfeit fraud, where a fake card is manufactured based on stolen card data, cost the UK £47.8m in 2014 according to figures just released by Financial Fraud Action. The cryptographic key embedded in chip cards tackles counterfeit fraud by allowing the card to prove its identity. Extracting this key should be very difficult, while copying the details embedded in a card’s magnetic stripe from one card to another is simple.

The second type of fraud is where a genuine card is used, but by the wrong person. Chip and PIN makes this more difficult by requiring users to enter a PIN code, one (hopefully) not known to the criminal who took the card. Financial Fraud Action separates this into those cards stolen before reaching their owner (at a cost of £10.1m in 2014) and after (£59.7m).

Unfortunately Chip and PIN doesn’t work as well as was hoped. My research has shown how it’s possible to trick cards into accepting the wrong PIN and produce cloned cards that terminals won’t detect as being fake. Nevertheless, the widespread introduction of Chip and PIN has succeeded in forcing criminals to change tactics – £331.5m of UK card fraud (69% of the total) in 2014 is now through telephone, internet and mail order purchases (known as “cardholder not present” fraud) that don’t involve the chip at all. That’s why there’s some surprise over the introduction of less secure contactless cards.

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