Category Archives: Three Paper Thursday

Three paper Thursday: COVID-19 and cybercrime

For a slightly different Three Paper Thursday, I’m pulling together some of the work done by our Centre and others around the COVID-19 pandemic and how it, and government responses to it, are reshaping the cybercrime landscape. 

The first thing to note is that there appears to be a nascent academic consensus emerging that the pandemic, or more accurately, lockdowns and social distancing, have indeed substantially changed the topology of crime in contemporary societies, leading to an increase in cybercrime and online fraud. The second is that this large-scale increase in cybercrime appears to be the result of a growth in existing cybercrime phenomena rather than the emergence of qualitatively new exploits, scams, attacks, or crimes. This invites reconsideration not only of our understandings of cybercrime and its relation to space, time, and materiality, but additionally to our understandings of what to do about it.

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Three Paper Thursday: Applying natural language processing to underground forums

Underground forums contain discussions and advertisements of various topics, including general chatter, hacking tutorials, and sales of items on marketplaces. While off-the-shelf natural language processing (NLP) techniques may be applied in this domain, they are often trained on standard corpora such as news articles and Wikipedia. 

It isn’t clear how well these models perform with the noisy text data found on underground forums, which contains evolving domain-specific lexicon, misspellings, slang, jargon, and acronyms. I explored this problem with colleagues from the Cambridge Cybercrime Centre and the Computer Laboratory, in developing a tool for detecting bursty trending topics using a Bayesian approach of log-odds. The approach uses a prior distribution to detect change in the vocabulary used in forums, for filtering out consistently used jargon and slang. The paper has been accepted to the 2020 Workshop on Noisy User-Generated Text (ACL) and the preprint is available online.

Other more commonly used approaches of identifying known and emerging trends range from simple keyword detection using a dictionary of known terms, to statistical methods of topic modelling including TF-IDF and Latent Dirichlet Allocation (LDA). In addition, the NLP landscape has been changing over the last decade [1], with a shift to deep learning using neural models, such as word2vec and BERT.

In this Three Paper Thursday, we look at how past papers have used different NLP approaches to analyse posts in underground forums, from statistical techniques to word embeddings, for identifying and define new terms, generating relevant warnings even when the jargon is unknown, and identifying similar threads despite relevant keywords not being known.

[1] Gregory Goth. 2016. Deep or shallow, NLP is breaking out. Commun. ACM 59, 3 (March 2016), 13–16. DOI:https://doi.org/10.1145/2874915

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Three Paper Thursday: Broken Hearts and Empty Wallets

This is a guest post by Cassandra Cross.

Romance fraud (also known as romance scams or sweetheart swindles) affects millions of individuals globally each year. In 2019, the Internet Crime Complaint Centre (IC3) (USA) had over US$475 million reported lost to romance fraud. Similarly, in Australia, victims reported losing over $AUD80 million and British citizens reported over £50 million lost in 2018. Given the known under-reporting of fraud overall, and online fraud more specifically, these figures are likely to only be a minority of actual losses incurred.

Romance fraud occurs when an offender uses the guise of a legitimate relationship to gain a financial advantage from their victim. It differs from a bad relationship, in that from the outset, the offender is using lies and deception to obtain monetary rewards from their partner. Romance fraud capitalises on the fact that a potential victim is looking to establish a relationship and exhibits an express desire to connect with someone. Offenders use this to initiate a connection and start to build strong levels of trust and rapport.

As with all fraud, victims experience a wide range of impacts in the aftermath of victimisation. While many believe these to be only financial, in reality, it extends to a decline in both physical and emotional wellbeing, relationship breakdown, unemployment, homelessness, and in extreme cases, suicide. In the case of romance fraud, there is the additional trauma associated with grieving both the loss of the relationship as well as any funds they have transferred. For many victims, the loss of the relationship can be harder to cope with than the monetary aspect, with victims experiencing large degrees of betrayal and violation at the hands of their offender.

Sadly, there is also a large amount of victim blaming that exists with both romance fraud and fraud in general. Fraud is unique in that victims actively participate in the offence, through the transfer of money, albeit under false pretences. As a result, they are seen to be culpable for what occurs and are often blamed for their own circumstances. The stereotype of fraud victims as greedy, gullible and naïve persists, and presents as a barrier to disclosure as well as inhibiting their ability to report the incident and access any support services.

Given the magnitude of losses and impacts on romance fraud victims, there is an emerging body of scholarship that seeks to better understand the ways in which offenders are able to successfully target victims, the ways in which they are able to perpetrate their offences, and the impacts of victimisation on the individuals themselves. The following three articles each explore different aspects of romance fraud, to gain a more holistic understanding of this crime type.

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Reinforcement Learning and Adversarial thinking

We all know that learning a new craft is hard. We spend a large part of our lives learning how to operate in everyday physics.  A large part of this learning comes from observing others, and when others can’t help we learn through trial and error. 

In machine learning the process of learning how to deal with the environment is called Reinforcement Learning (RL). By continuous interaction with its environment, an agent learns a policy that enables it to perform better. Observational learning in RL is referred to as Imitation Learning. Both trial and error and imitation learning are hard: environments are not trivial, often you can’t tell the ramifications of an action until far in the future, environments are full of non-determinism and there are no such thing as a correct policy. 

So, unlike in supervised and unsupervised learning, it is hard to tell if your decisions are correct. Episodes usually constitute thousands of decisions, and you will only know if you perform well after exploring other options. But experiment is also a hard decision: do you exploit the skill you already have, or try something new and explore the unknown?

Despite all these complexities, RL has managed to achieve incredible performance in a wide variety of tasks from robotics through recommender systems to trading. More impressively, RL agents have achieved superhuman performance in Go and other games, tasks previously believed to be impossible for computers. 

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Three paper Thursday: Ethics in security research

Good security and cybercrime research often creates an impact and we want to ensure that impact is positive. This week I will discuss three papers on ethics in computer security research in the run up to next week’s Security and Human Behaviour workshop (SHB). Ethical issues in research using datasets of illicit origin (Thomas, Pastrana, Hutchings, Clayton, Beresford) from IMC 2017, Measuring eWhoring (Pastrana, Hutchings, Thomas, Tapiador) from IMC 2019, and An Ethics Framework for Research into Heterogeneous Systems (Happa, Nurse, Goldsmith, Creese, Williams).

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:

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Three Paper Thursday – Analysing social networks within underground forums

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.

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Three Paper Thursday: Vulnerabilities! We’ve got vulnerabilities here! … See? Nobody cares.

Jurassic Park is often (mistakenly) left out of the hacker movie canon. It clearly demonstrated the risk of an insider attack on control systems (Velociraptor rampage, amongst other tragedies…) nearly a decade ahead of the Maroochy sewage incident, it’s the first film I know of with a digital troll (“ah, ah, ah, you didn’t say the magic word!”), and Samuel L. Jackson correctly assesses the possible consequence of a hard reset (namely, everyone dying), resulting in his legendary “Hold on to your butts”. The quotable mayhem is seeded early in the film, when biotech spy Lewis Dodgson gives a sack of money to InGen’s Dennis Nedry to steal some dino DNA. Dodgson’s caricatured OPSEC (complete with trilby and dark glasses) is mocked by Nedry shouting, “Dodgson! Dodgson! We’ve got Dodgson here! See, nobody cares…” Three decades later, this quote still comes to mind* whenever conventional wisdom doesn’t seem to square with observed reality, and today we’re going to apply it to the oft-maligned world of Industrial Control System (ICS) security.

There is plenty of literature on ICS security pre-2010, but people really sat up and started paying attention when we learned about Stuxnet. Possibly the most upsetting thing about Stuxnet (for security-complacent control system designers like me) was the apparent ease with which the “air gap” was bridged over and over again. Any remaining faith in the air gap was killed by Éireann Leverett’s demonstration (thesis and S4 presentation) that thousands of industrial systems were directly connected to the Internet — no air gap jumping required. Since then, we’ve observed a steady growth in Internet-connected ICS devices, due both to improved search techniques and increasingly-connectable ICS devices. On any given day you can find about 100,000 unique devices speaking industrial protocols on Censys and Shodan. These protocols are largely unauthenticated and unencrypted, allowing an attacker that can speak the protocol to remotely read state, issue commands, and even modify programmable logic without using an actual exploit.

This sounds (and is) bad, and people have (correctly) highlighted its badness on many occasions. The attacks, however, appear to be missing: we are not aware of a single instance of industrial damage initiated via an Internet-connected ICS device. In this Three Paper Thursday we’ll look at papers showing how easy it is to find and contextualise Internet-connected ICS devices, some evidence for lack of malicious interest, and some leading indicators that this happy conclusion (for which we don’t really deserve any credit) may be changing.

*Perhaps because guys of a certain age still laugh and say “Dodson! We’ve got Dodson here!” when they learn my surname. I try to explain that it’s spelt differently, but…
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Three Paper Thursday – GDPR anniversary edition

This is a guest contribution from Daniel Woods.

This coming Monday will mark two years since the General Data Protection Regulation (GDPR) came into effect. It prompted an initial wave of cookie banners that drowned users in assertions like “We value your privacy”. Website owners hoped that collecting user consent would ensure compliance and ward off the lofty fines.

Article 6 of the GDPR describes how organisations can establish a legal basis for processing personal data. Putting aside a selection of `necessary’ reasons for doing so, data processing can only be justified by collecting the user’s consent to “the processing of his or her personal data for one or more specific purposes”. Consequently, obtaining user consent could be the difference between suffering a dizzying fine or not.

The law changed the face of the web and this post considers one aspect of the transition. Consent Management Providers (CMPs) emerged offering solutions for websites to embed. Many of these use a technical standard described in the Transparency and Consent Framework. The standard was developed by the Industry Advertising Body, who proudly claim it is is “the only GDPR consent solution built by the industry for the industry”.

All of the following studies either directly measure websites implementing this standard or explore the theoretical implications of standardising consent. The first paper looks at how the design of consent dialogues shape the consent signal sent by users. The second paper identifies disparities between the privacy preferences communicated via cookie banners and the consent signals stored by the website. The third paper uses coalitional game theory to explore which firms extract the value from consent coalitions in which websites share consent signals.

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Three Paper Thursday: Will we ever get IoT security right?

Academia, governments and industry frequently talk about the importance of IoT security. Fundamentally, the IoT environment has similar problems to other technology platforms such as Android: a fragmented market with no clear responsibilities or incentives for vendors to provide regular updates, and consumers for whom its not clear how much (of a premium) they are willing to pay for (“better”) security and privacy.

Just two weeks ago, Belkin announced to shut down one of its cloud services, effectively transforming its several product lines of web cameras into useless bricks. Unlike other end-of-support announcements for IoT devices that (only) mean devices will never see an update again, many Belkin cameras simply refuse to work without the “cloud”. This is particularly disconcerting  as many see cloud-based IoT as one possible solution to improve device security by easing the user maintenance effort through remote update capabilities.

In this post, I would like to introduce three papers, each talking about different aspects of IoT security: 1) consumer purchasing behaviour, 2) vendor response, and 3) an assessment of the ever-growing literature on “best-practices” from industrial, governmental, and academic sources.
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Three Paper Thursday: What’s Intel SGX Good For?

Software Guard eXtensions (SGX) represents Intel’s latest foray into trusted computing. Initially intended as a means to secure cloud computation, it has since been employed for DRM and secure key storage in production systems. SGX differs from its competitors such as TrustZone in its focus on reducing the volume of trusted code in its “secure world”. These secure worlds are called enclaves in SGX parlance and are protected from untrusted code by a combination of a memory encryption engine and a set of new CPU instructions to enforce separation.

SGX has been available on almost every Intel processor since the sixth generation of Intel Core (Skylake). It has had a bit of a rocky journey since. Upon its release, there were concerns expressed about SGX being used as a medium for malware dissemination. A proof of concept demonstrating the effectiveness of this was also published. Then there were concerns about the mandatory agreements required to publish enclave code in production. In order to use Intel’s attestation service, a key enabler for any remote application built on SGX, developers have to obtain a commercial license from Intel. This lock-in raised a fair bit of consternation and to date hasn’t been fully addressed by Intel. Then came the vulnerabilities. SGX was found to be vulnerable to cache timing attacks speculative execution vulnerabilities and to Load Value Injection attacks. This has further eroded the credibility of the platform.

That said, there have been a fair few projects that have utilized SGX either to harden existing solutions or to come up with hitherto unseen applications. In this post, we’ll look at three such proposals to see how they utilize SGX despite its concerning attributes. Where applicable, we will also talk about how the discovered vulnerabilities have affected their viability.

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