I’m at the fourth Cambridge Cybercrime Conference, which I will try to liveblog in followups to this post.
I’ll be trying to liveblog the twelfth workshop on security and human behaviour at Harvard. I’m doing this remotely because of US visa issues, as I did for WEIS 2019 over the last couple of days. Ben Collier is attending as my proxy and we’re trying to build on the experience of telepresence reported here and here. My summaries of the workshop sessions will appear as followups to this post.
I’ll be trying to liveblog the seventeenth workshop on the economics of information security at Harvard. I’m not in Cambridge, Massachussetts, but in Cambridge, England, because of a visa held in ‘administrative processing’ (a fate that has befallen several other cryptographers). My postdoc Ben Collier is attending as my proxy (inspired by this and this).
In 2012 we presented the first systematic study of the costs of cybercrime. We have now repeated our study, to work out what’s changed in the seven years since then.
Measuring the Changing Cost of Cybercrime will appear on Monday at WEIS. The period has seen huge changes, with the smartphone replacing as PC and laptop as the consumer terminal of choice, with Android replacing Windows as the most popular operating system, and many services moving to the cloud. Yet the overall pattern of cybercrime is much the same.
We know a lot more than we did then. Back in 2012, we guessed that cybercrime was about half of all crime, by volume and value; we now know from surveys in several countries that this is the case. Payment fraud has doubled, but fallen slightly as a proportion of payment value; the payment system has got larger, and slightly more efficient.
So what’s changed? New cybercrimes include ransomware and other offences related to cryptocurrencies; travel fraud has also grown. Business email compromise and its cousin, authorised push payment fraud, are also growth areas. We’ve also seen serious collateral damage from cyber-weapons such as the NotPetya worm. The good news is that crimes that infringe intellectual property – from patent-infringing pharmaceuticals to copyright-infringing software, music and video – are down.
Our conclusions are much the same as in 2012. Most cyber-criminals operate with impunity, and we have to fix this. We need to put a lot more effort into catching and punishing the perpetrators.
When you visit a website, your web browser provides a range of information to the website, including the name and version of your browser, screen size, fonts installed, and so on. Website authors can use this information to provide an improved user experience. Unfortunately this same information can also be used to track you. In particular, this information can be used to generate a distinctive signature, or device fingerprint, to identify you.
We have developed a new type of fingerprinting attack, the calibration fingerprinting attack. Our attack uses data gathered from the accelerometer, gyroscope and magnetometer sensors found in smartphones to construct a globally unique fingerprint. Our attack can be launched by any website you visit or any app you use on a vulnerable device without requiring any explicit confirmation or consent from you. The attack takes less than one second to generate a fingerprint which never changes, even after a factory reset. This attack therefore provides an effective means to track you as you browse across the web and move between apps on your phone.
Our approach works by carefully analysing the data from sensors which are accessible without any special permissions on both websites and apps. Our analysis infers the per-device factory calibration data which manufacturers embed into the firmware of the smartphone to compensate for systematic manufacturing errors. This calibration data can then be used as the fingerprint.
In general, it is difficult to create a unique fingerprint on iOS devices due to strict sandboxing and device homogeneity. However, we demonstrated that our approach can produce globally unique fingerprints for iOS devices from an installed app: around 67 bits of entropy for the iPhone 6S. Calibration fingerprints generated by a website are less unique (around 42 bits of entropy for the iPhone 6S), but they are orthogonal to existing fingerprinting techniques and together they are likely to form a globally unique fingerprint for iOS devices. Apple adopted our proposed mitigations in iOS 12.2 for apps (CVE-2019-8541). Apple recently removed all access to motion sensors from Mobile Safari by default.
Jiexin Zhang, Alastair R. Beresford and Ian Sheret, SensorID: Sensor Calibration Fingerprinting for Smartphones, Proceedings of the 40th IEEE Symposium on Security and Privacy (S&P), 2019.
I’m writing a third edition of my best-selling book Security Engineering. The chapters will be available online for review and feedback as I write them.
Today I put online a chapter on Who is the Opponent, which draws together what we learned from Snowden and others about the capabilities of state actors, together with what we’ve learned about cybercrime actors as a result of running the Cambridge Cybercrime Centre. Isn’t it odd that almost six years after Snowden, nobody’s tried to pull together what we learned into a coherent summary?
There’s also a chapter on Surveillance or Privacy which looks at policy. What’s the privacy landscape now, and what might we expect from the tussles over data retention, government backdoors and censorship more generally?
There’s also a preface to the third edition.
As the chapters come out for review, they will appear on my book page, so you can give me comment and feedback as I write them. This collaborative authorship approach is inspired by the late David MacKay. I’d suggest you bookmark my book page and come back every couple of weeks for the latest instalment!
Security systems are often designed by geeks who assume that the users will also be geeks, and the same goes for the advice that users are given when things start to go wrong. For example, banks reacted to the growth of phishing in 2006 by advising their customers to parse URLs. That’s fine for geeks but most people don’t do that, and in particular most women don’t do that. So in the second edition of my Security Engineering book, I asked (in chapter 2, section 2.3.4, pp 27-28): “Is it unlawful sex discrimination for a bank to expect its customers to detect phishing attacks by parsing URLs?”
Tyler Moore and I then ran the experiment, and Tyler presented the results at the first Workshop on Security and Human Behaviour that June. We recruited 132 volunteers between the ages of 18 and 30 (77 female, 55 male) and tested them to see whether they could spot phishing websites, as well as for systematising quotient (SQ) and empathising quotient (EQ). These measures were developed by Simon Baron-Cohen in his work on Asperger’s; most men have SQ > EQ while for most women EQ > SQ. The ability to parse URLs is correlated with SQ-EQ and independently with gender. A significant minority of women did badly at URL parsing. We didn’t get round to publishing the full paper at the time, but we’ve mentioned the results in various talks and lectures.
We have now uploaded the original paper, How brain type influences online safety. Given the growing interest in gender HCI, we hope that our study might spur people to do research in the gender aspects of security as well. It certainly seems like an open goal!
I’m in the Security Protocols Workshop, whose theme this year is “security protocols for humans”. I’ll try to liveblog the talks in followups to this post.
Have you ever wondered whether one app on your phone could spy on what you’re typing into another? We have. Five years ago we showed that you could use the camera to measure the phone’s motion during typing and use that to recover PINs. Then three years ago we showed that you could use interrupt timing to recover text entered using gesture typing. So what other attacks are possible?
Our latest paper shows that one of the apps on the phone can simply record the sound from its microphones and work out from that what you’ve been typing.
Your phone’s screen can be thought of as a drum – a membrane supported at the edges. It makes slightly different sounds depending on where you tap it. Modern phones and tablets typically have two microphones, so you can also measure the time difference of arrival of the sounds. The upshot is that can recover PIN codes and short words given a few measurements, and in some cases even long and complex words. We evaluate the new attack against previous ones and show that the accuracy is sometimes even better, especially against larger devices such as tablets.
This paper is based on Ilia Shumailov’s MPhil thesis project.