“Do you see what I see?” ask Tor users, as a large number of websites reject them but accept non-Tor users

If you use an anonymity network such as Tor on a regular basis, you are probably familiar with various annoyances in your web browsing experience, ranging from pages saying “Access denied” to having to solve CAPTCHAs before continuing. Interestingly, these hurdles disappear if the same website is accessed without Tor. The growing trend of websites extending this kind of “differential treatment” to anonymous users undermines Tor’s overall utility, and adds a new dimension to the traditional threats to Tor (attacks on user privacy, or governments blocking access to Tor). There is plenty of anecdotal evidence about Tor users experiencing difficulties in browsing the web, for example the user-reported catalog of services blocking Tor. However, we don’t have sufficient detail about the problem to answer deeper questions like: how prevalent is differential treatment of Tor on the web; are there any centralized players with Tor-unfriendly policies that have a magnified effect on the browsing experience of Tor users; can we identify patterns in where these Tor-unfriendly websites are hosted (or located), and so forth.

Today we present our paper on this topic: “Do You See What I See? Differential Treatment of Anonymous Users” at the Network and Distributed System Security Symposium (NDSS). Together with researchers from the University of Cambridge, University College London, University of California, Berkeley and International Computer Science Institute (Berkeley), we conducted comprehensive network measurements to shed light on websites that block Tor. At the network layer, we scanned the entire IPv4 address space on port 80 from Tor exit nodes. At the application layer, we fetch the homepage from the most popular 1,000 websites (according to Alexa) from all Tor exit nodes. We compare these measurements with a baseline from non-Tor control measurements, and uncover significant evidence of Tor blocking. We estimate that at least 1.3 million IP addresses that would otherwise allow a TCP handshake on port 80 block the handshake if it originates from a Tor exit node. We also show that at least 3.67% of the most popular 1,000 websites block Tor users at the application layer.

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Scaling Tor hidden services

Tor hidden services offer several security advantages over normal websites:

  • both the client requesting the webpage and the server returning it can be anonymous;
  • websites’ domain names (.onion addresses) are linked to their public key so are hard to impersonate; and
  • there is mandatory encryption from the client to the server.

However, Tor hidden services as originally implemented did not take full advantage of parallel processing, whether from a single multi-core computer or from load-balancing over multiple computers. Therefore once a single hidden service has hit the limit of vertical scaling (getting faster CPUs) there is not the option of horizontal scaling (adding more CPUs and more computers). There are also bottle-necks in the Tor networks, such as the 3–10 introduction points that help to negotiate the connection between the hidden service and the rendezvous point that actually carries the traffic.

For my MSc Information Security project at UCL, supervised by Steven Murdoch with the assistance of Alec Muffett and other Security Infrastructure engineers at Facebook in London, I explored possible techniques for improving the horizontal scalability of Tor hidden services. More precisely, I was looking at possible load balancing techniques to offer better performance and resiliency against hardware/network failures. The focus of the research was aimed at popular non-anonymous hidden services, where the anonymity of the service provider was not required; an example of this could be Facebook’s .onion address.

One approach I explored was to simply run multiple hidden service instances using the same private key (and hence the same .onion address). Each hidden service periodically uploads its own descriptor, which describes the available introduction points, to six hidden service directories on a distributed hash table. The hidden service instance chosen by the client depends on which hidden service instance most recently uploaded its descriptor. In theory this approach allows an arbitrary number of hidden service instances, where each periodically uploads its own descriptors, overwriting those of others.

This approach can work for popular hidden services because, with the large number of clients, some will be using the descriptor most recently uploaded, while others will have cached older versions and continue to use them. However my experiments showed that the distribution of the clients over the hidden service instances set up in this way is highly non-uniform.

I therefore ran experiments on a private Tor network using the Shadow network simulator running multiple hidden service instances, and measuring the load distribution over time. The experiments were devised such that the instances uploaded their descriptors simultaneously, which resulted in different hidden service directories receiving different descriptors. As a result, clients connecting to a hidden service would be balanced more uniformly over the available instances.

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What are the social costs of contactless fraud?

Contactless payments are in the news again: in the UK the spending limit has been increased from £20 to £30 per transaction, and in Australia the Victoria Police has argued that contactless payments are to blame for an extra 100 cases of credit card fraud per week. These frauds are where multiple transactions are put through, keeping each under the AUS $100 (about £45) limit. UK news coverage has instead focussed on the potential for cross-channel fraud: where card details are skimmed from contactless cards then used for fraudulent online purchases. In a demonstration, Which? skimmed volunteers cards at a distance then bought a £3,000 TV with the card numbers and expiry dates recorded.

The media have been presenting contactless payments are insecure; the response from the banking industry is to point out that customers are not liable for the fraudulent transactions. Both are in some ways correct, but in other ways are missing the point.

The law in the UK (Payment Services Regulations (PSR) 2009, Regulation 62) indeed does say that the customers are entitled to a refund for fraudulent transactions. However a bank will only do this if they are convinced the customer has not authorised the transaction, and was not negligent. In my experience, a customer who is unable to clearly, concisely and confidently explain why they are entitled to a refund runs a high risk of not getting one. This fact will disproportionately disadvantage the more vulnerable members of society.

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Experimenting with SSL Vulnerabilities in Android Apps

As the number of always-on, always-connected smartphones increase, so does the amount of personal and sensitive information they collect and transmit. Thus, it is crucial to secure traffic exchanged by these devices, especially considering that mobile users might connect to open Wi-Fi networks or even fake cell towers. The go-to protocol to secure network connection is HTTPS i.e., HTTP over SSL/TLS.

In the Android ecosystem, applications (apps for short), support HTTPS on sockets by relying on the android.net, android.webkit, java.net, javax.net, java.security, javax.security.cert, and org.apache.http packages of the Android SDK. These packages are used to create HTTP/HTTPS connections, administer and verify certificates and keys, and instantiate TrustManager and HostnameVerifier interfaces, which are in turn used in the SSL certificate validation logic.

A TrustManager manages the certificates of all Certificate Authorities (CAs) used to assess a certificate’s validity. Only root CAs trusted by Android are contained in the default TrustManager. A HostnameVerifier performs hostname verification whenever a URL’s hostname does not match the hostname in the peer’s identification credentials.

While browsers provide users with visual feedback that their communication is secured (via the lock symbol) as well as certificate validation issues, non-browser apps do so less extensively and effectively. This shortcoming motivates the need to scrutinize the security of network connections used by apps to transmit user sensitive data. We found that some of the most popular Android apps insufficiently secure these connections, putting users’ passwords, credit card details and chat messages at risk.

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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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