When you are a medical doctor, friends and family invariably ask you about their aches and pains. When you are a computer specialist, they ask you to fix their computer. About ten years ago, most of the questions I was getting from friends and family as a security techie had to do with frustration over passwords. I observed that what techies had done to the rest of humanity was not just wrong but fundamentally unethical: asking people to do something impossible and then, if they got hacked, blaming them for not doing it.
So in 2011, years before the Fido Alliance was formed (2013) and Apple announced its smartwatch (2014), I published my detailed design for a clean-slate password replacement I calledPico, an alternative system intended to be easier to use and more secure than passwords. The European Research Council was generous enough to fund my vision with a grant that allowed me to recruit and lead a team of brilliant researchers over a period of five years. We built a number of prototypes, wrote a bunch of papers, offered projects to a number of students and even launched a start-up and thereby learnt a few first-hand lessons about business, venture capital, markets, sales and the difficult process of transitioning from academic research to a profitable commercial product. During all those years we changed our minds a few times about what ought to be done and we came to understand a lot better both the problem space and the mindset of the users.
Ethical issues in research using datasets of illicit origin (blog post) came about because in prior work we had noticed that there were ethical complexities to take care of when using data that had “fallen off the back of a lorry” such as the backend databases of hacked booter services that we had used. We took a broad look at existing published guidance to synthesise those issues which particularly apply to using data of illicit origin and we expected to see discussed by researchers:
Deep neural networks (DNNs) have been a very active field of research for eight years now, and for the last five we’ve seen a steady stream of adversarial examples – inputs that will bamboozle a DNN so that it thinks a 30mph speed limit sign is a 60 instead, and even magic spectacles to make a DNN get the wearer’s gender wrong.
So far, these attacks have targeted the integrity or confidentiality of machine-learning systems. Can we do anything about availability?
Sponge Examples: Energy-Latency Attacks on Neural Networks shows how to find adversarial examples that cause a DNN to burn more energy, take more time, or both. They affect a wide range of DNN applications, from image recognition to natural language processing (NLP). Adversaries might use these examples for all sorts of mischief – from draining mobile phone batteries, though degrading the machine-vision systems on which self-driving cars rely, to jamming cognitive radar.
So far, our most spectacular results are against NLP systems. By feeding them confusing inputs we can slow them down over 100 times. There are already examples in the real world where people pause or stumble when asked hard questions but we now have a dependable method for generating such examples automatically and at scale. We can also neutralize the performance improvements of accelerators for computer vision tasks, and make them operate on their worst case performance.
One implication is that engineers designing real-time systems that use machine learning will have to pay more attention to worst-case behaviour; another is that when custom chips used to accelerate neural network computations use optimisations that increase the gap between worst-case and average-case outcomes, you’d better pay even more attention.
One would be hard pressed to find an aspect of life where networks are not present. Interconnections are at the core of complex systems – such as society, or the world economy – allowing us to study and understand their dynamics. Some of the most transformative technologies are based on networks, be they hypertext documents making up the World Wide Web, interconnected networking devices forming the Internet, or the various neural network architectures used in deep learning. Social networks that are formed based on our interactions play a central role in our every day lives; they determine how ideas and knowledge spread and they affect behaviour. This is also true for cybercriminal networks present on underground forums, and social network analysis provides valuable insights to how these communities operate either on the dark web or the surface web.
For today’s post in the series `Three Paper Thursday’, I’ve selected three papers that highlight the valuable information we can learn from studying underground forums if we model them as networks. Network topology and large scale structure provide insights to information flow and interaction patterns. These properties along with discovering central nodes and the roles they play in a given community are useful not only for understanding the dynamics of these networks but for various purposes, such as devising disruption strategies.
We are specifically interested in extending our data collection to better record how cybercrime has changed in response the COVID-19 pandemic and we wish to mine our datasets in order to understand whether cybercrime has increased, decreased or displaced during 2020.
There are a lot of theories being proposed as to what may or may not have changed, often based on handfuls of anecdotes — we are looking for researchers who will help us provide data driven descriptions of what is (now) going on — which will feed into policy debates as to the future importance of cybercrime and how best to respond to it.
We are not necessarily looking for existing experience in researching cybercrime, although this would be a bonus. However, we are looking for strong programming skills — and experience with scripting languages and databases would be much preferred. Good knowledge of English and communication skills are important.
Since these posts are only guaranteed to be funded until the end of September, we will be shortlisting candidates for (online) interview as soon as possible (NOTE the application deadline is less than ONE WEEK AWAY) and will be giving preference to people who can take up a post without undue delay. The rapid timescale of the hiring process means that we will only be able to offer positions to candidates who already have permission to work in the UK (which, as a rough guide, means UK or EU citizens or those with existing appropriate visas).
We do not realistically expect to be permitted to return to our desks in the Computer Laboratory before the end of September, so it will be necessary for successful candidates to be able to successfully “work from home” … not necessarily within the UK.
Please follow this link to the advert to read the formal advertisement for the details about exactly who and what we’re looking for and how to apply.
Much has been made in the cybersecurity literature of the transition of cybercrime to a service-based economy, with specialised services providing Denial of Service attacks, cash-out services, escrow, forum administration, botnet management, or ransomware configuration to less-skilled users. Despite this acknowledgement of the ‘industrialisation’ of much for the cybercrime economy, the picture of cybercrime painted by law enforcement and media reports is often one of ’sophisticated’ attacks, highly-skilled offenders, and massive payouts. In fact, as we argue in a recent paper accepted to the Workshop on the Economics of Information Security this year (and covered in KrebsOnSecurity last week), cybercrime-as-a-service relies on a great deal of tedious, low-income, and low-skilled manual administrative work.