Initially, this was to form part of Mitigate Cyber’s suite of Mitigate Hub modules. However, their unexpected early withdrawal led instead to similar ideas being explored within a game setting.

I also delivered a pair of presentations on the theory behind this project whilst working at Actica.

I worked on the Mitilearn module as part of KTP № 11598 with Lancaster Universitiy.

I worked on the Mitigate Hub platform itself, along with the Mitilearn e-learning and Mitiintel threat intelligence modules, as part of KTP № 11598 with Lancaster University.

• LL.M5230: Introduction to Law
• PPR.432: Security and Conflict in the Digital Age
• SCC.420: MSc. Dissertation
• SCC.439: Network and Systems Security
• SCC.441: Information System Security Management
• SCC.442: Information System Penetration and Countermeasures
• SCC.443: Information System Forensic Investigation
• SCC.444: Information System Risk Management
• SOCL491: Cybercrime

Abstract ¶

DẼMOS 2 is a proposed e-voting system that is end-to-end verifiable, meaning that its results can be verified for correctness from end-to-end. This is clearly an important characteristic of any e-voting system that aspires to widespread adoption, but it requires that voters and third parties possess the means to perform such verification. It is in this area that the current DẼMOS 2 implementation is lacking, and this project is an attempt to rectify this through the development of a mobile phone app. for Android devices. The design of such an app. is here presented, but the implementation remains incomplete. Despite this, an evaluation of the app. as it currently stands is presented, followed by a detailing of avenues of further development. Ultimately, however, the supposed benefits of e-voting are brought into question.

Abstract ¶

DẼMOS 2 is a proposed e-voting system that is end-to-end verifiable, meaning that its results can be verified for correctness from end-to-end. This is clearly an important characteristic of any e-voting system that aspires to widespread adoption, but it requires that voters and third parties possess the means to perform such verification. It is in this area that the current DẼMOS 2 implementation is lacking, and this project is an attempt to rectify this through the development of a mobile phone app. for Android devices. The design of such an app. is here presented, but the implementation remains incomplete. Despite this, an evaluation of the app. as it currently stands is presented, followed by a detailing of avenues of further development. Ultimately, however, the supposed benefits of e-voting are brought into question.

Abstract ¶

In this essay, the various types of anti-forensic techniques are categorised and example tools of each given. Then, the three that are considered to pose the greatest threat to the forensic analyst and their work are further analysed, along with the responses to each from the digital forensics community. Finally, possible futures for forensics and anti-forensics are suggested. Whilst the use of anti-forensic tools is examined from the point-of-view of a criminal user attempting to evade law enforcement, it is worth noting that such tools can also be used to protect activists from prosecution by authoritarian regimes where such a regime is still nonetheless bound by a need to present evidence to back their charges, and to protect their connections and allies where such a regime is not.

As far as further development, I did once entertain the idea of trying to store a small amount of Bitcoin on the micro:bit, selling the micro:bit for slightly above the worth of the Bitcoin at the time of sale and having the micro:bit transfer portions of its coin over to the user as they achieved daily step goals. It’s not hard to see why that never came to fruition.

Abstract ¶

In this essay, the relevance of the theory of High-Reliability Organisations (HRO) to the provision and risk assessment of information security services is assessed. The two main variants of HRO theory are discussed, and an HRO’s potential approach to information security is detailed. This is followed by an analysis of the flaws in, and proposed alternatives to, HRO theory. Finally, the pros and cons of each of these competing theories in their application to information security are considered. Ultimately, a synthesis of them all is proposed as the most useful, as each brings something useful to the table, and the urgency of further research is reinforced.

Abstract ¶

The Open Web Application Security Project (OWASP) is an attempt to improve the general level of web application security across all industries via the production of educational materials, and other projects. One such project is the OWASP Top Ten, detailing what the Project considers to be the ten most pervasive web application security vulnerabilities at a given time. This report shall first summarise the OWASP itself, and then the Top Ten project and its methodolgy. The 2013 instalment shall be detailed, followed by a critical analysis of its choices. A number of suggestions shall then be made for a proposed 2017 update. Though this report shall be written from the perspective of an official OWASP Top Ten for 2017 not yet having been released, this is in fact not the case. Other than this mention, the 2017 Top Ten is considered to be outside of the scope of this report.

I don’t care who writes the rules, as long as someone writes the rules that we can then verify.

Prof. Michael Fisher of the University of Liverpool came to the University of Lancaster on 2017-12-13 to present an installment of the SCC Distinguished Seminar Series. The title of the talk was Responsible Autonomy: AI, safety, ethics and legality.

Arriving a few minutes in, Prof. Fisher was already explaining the concept of hybrid agent architectures—he would later return to this in detail, so nothing was lost. An autonomous system was defined as one that makes decisions without human intervention and that is automatic, adaptive and autonomous. Prof. Fisher showed the room adverts for the Care-O-Bot 3 & 4 in order to drive home how quickly advancements were being made. Following this, Prof. Fisher presented the central issue of the talk: Would you trust a fully autonomous system? He then followed it up—as is required by law in discussions on this sort of topic—with a picture of a T-800.

Prof. Fisher now returned to the idea of the hybrid agent architecture. In such an architecture, the agent is divided into a traditional feedback control system block for low-level behaviour and a rational agent for high-level. The idea was roughly analogous to a human mind thinking I want to be over there, and instinct taking care of the nitty-gritty of the actual locomotion. A rational agent, Prof. Fisher insisted, must have explicit reasons for making choices and be able to explain them. Another analogue was presented of an airplane autopilot handling the mechanics of flight, whilst a human pilot decided elements such as the route, and was present in case of emergency. Prof. Fisher’s central argument was that the rational agent component must be formally verified. For example, if the Rules of the Air were converted into formal logic, it must be verified that a rational agent will always make the same choice as a good pilot.

The most interesting issue raised, to my mind, was that of ethical issues. Ethical reasoning was said to be invoked when the rational agent was presented with conflicting solutions, no solutions or when danger was involved. Prof. Fisher briefly illustrated the point with an example of a UAV deciding whether to crash land in a school full of children, a field full of animals or an empty road. This kind of trolley problem issue is commonplace in discussions of autonomous systems. Usually, it is assumed that the government shall come up with some codified moral code that all devices shall have to follow. However, this seems unlikely. Over thousands of years of human development, we still haven’t figured out a universal moral code. Sure, ensuring that autonomous systems will always prioritise human life seems like a given, but what about after the military inevitably give machines the right to pull their own triggers?

Let’s say, though, that this happens. HM Government come out with an official British moral code. What happens when the US put out their own code? And Saudi Arabia their own? Will an autonomous car purchased in the latter country adhere to their moral code and choose to veer into gays or women if given the choice between them or real people? Will you have to download a new moral code each time you change country, like a clock automatically changing to the correct timezone?

Another, more surreal thought that occurred to me was what might happen if the moral standardisation doesn’t occur at a national level. What if the free market is left to sort it out and each vendor can come up with their own system of morality? Might we see Samsung’s driverless car competing with Apple’s on the basis that the former comes complete with Confucian morality that will prioritise the elderly over the young when deciding who to crash into? The iPhone XX coming in small, large and Kantian versions?

Prof. Fisher had no answers for my unasked questions. He did, however, have some strong words for the current trend of learning algorithms and neural networks that produce decisions without the thought process being auditable. It’s irresponsible. You’ve tested it a few times—so what? You have no idea what it’s going to do when you put it in the real world. All in all, I have to concur—I don’t think we’ll ever be able to trust anything we can’t audit. More interesting, though, is what will happen when you can jailbreak your phone by importing a black market Nihilism module that kills the God in the machine and abolishes its pre-existing notion of right and wrong.

Abstract ¶

WPA2, the 14-year-old protocol that provides secure Wi-Fi connectivity, the security of which had been formally proven, has been kracked. It has been shown to be fundamentally insecure at a specification level. This translates to different degrees of impact at the implementation level, depending on decisions taken by different vendors. That this attack uses a novel mechanism also suggests that a review of other key-based security protocols may turn up further vulnerable examples. Perhaps most crucially, this attack raises important questions about the writing of standards and access to them. This report shall establish the necessary background knowledge, summarise the findings of Vanhoef & Piessens (2017) and analyse their likely impact in the real world.

Abstract ¶

The EU’s General Data Protection Regulation (GDPR) (a.k.a. Regulation (EU) 2016/679) comes into effect on May 25^th of next year. Much has been written about this unprecedentedly pro-consumer data protection legislation, not least of all regarding its applicability (or perhaps not) to a post-Brexit Britain. As an institution that caters to large numbers of EU and non-EU citizens each year, the University of Lancaster is in a potentially particularly difficult position. In this report, I shall briefly outline the history behind the GDPR before detailing the areas most applicable to the University. These technical, organisational and legal implications will, to the best of my ability, be considered with an eye to Britain’s leaving of the European Union on March 29th 2019, just under a year after GDPR comes into effect.

My supervisor was Dr Barry Porter.

I don’t think anyone in this room knows what loving grace is. You’re all engineers.

Prof. McCann opened her instalment of the SCC Distinguished Seminar Series with a brief excerpt from Stephenson’s The Diamond Age: Or, A Young Lady’s Illustrated Primer, which formed the central pillar of her speech. The excerpt talked about two pieces of fictional technology in the world of The Diamond Age; the matter compiler and smart dust. The latter are intelligent, self-organising atoms that float around in the book’s universe until they are sucked in my a matter compiler to create any substance or object the user desires. She asked if, though we were seeing context-aware smart computing, we were anywhere close to smart dust.

She then went through the challenges facing current smart devices. Firstly, they are largely low-resourced and flaky due to a need to be low-powered and untethered. If you’re digging into the ground to put something under it, you don’t want to be digging it up again later to replace the batteries. This tendency to degrade over time is also an issue as many smart device applications are intended to be used long-term.

The issue of how to get these low-powered devices to have a manageable-but-still-useful view of network was achieved by looking at nature, but not stealing from nature in the form of fish shoals and fireflies. The latter look at their next-closest neighbour to sync their flashing with, which before long leads to a whole swarm pulsing in unison. This distributed networking had the advantage of being hugely scalable.

Nodes don’t always need to be running 24/7, however, but how can they be allowed to sleep when they may be called upon to assist the network that they are a part of at any time? The solution was to sync the nodes to only sleep at times when the network expected it — slot synchronisation.

The second issue is that a node cannot eat food — or rather battery power — to regain energy lost through exertion. The answer to this is energy neutral operation, where energy (e.g. solar, wind, vibration, etc.) is harvested to nullify the need for batteries. This can only be used, however, when the system has bottom-line QoS, with the example given of an atmospheric sensing system — a sample can be missed due to upload/download throttling without having too much impact.

The third obstacle is that the systems are bounded by the physical world. For example, a city may have over a billion devices operating within its walls, which means the city will have to be viewed as a whole and Shannon capacity taken into account.

Prof. McCann then demonstrated an interesting opportunistic network she had been developing in order to get around this. In the system, users run an app in the background of their phone to which sensors (in the example given located in Central Park) chirp and send sensor data. When this user walks past another user, the data hops across to their phone until it can find a free Wi-Fi upload point or some other path to its destination. To incentivise users to sacrifice their battery life and run the app, a financial reward was given for each bit of data sucked up and ferried along.

Explaining the final issue — the nature of cyberphysical systems — Prof. McCann explained that noise is not always noise. One can trim sensors when the network itself can be used to sense — in the example given, a simple sensor to monitor whether a machine was running correctly was placed next to it, and the disruption caused by the machine operating used to determine if it was doing so, rather than a more complex sensor inside the machine.

Of course, in the wake of the Mirai botnet, security in the IoT remained a topical issue. Opening with another Diamond Age quote — In an era when everything can be surveiled, all we have left is politeness — Prof. McCann detailed some of the issue that these systems face. Malicious and non-malicious data injections need to be separated from valid, unexpected results. This can usually be done by testing the surrounding nodes to see if they’re witnessing the same thing, again a la the fireflies, but this reliance on neighbours opens up a new attack vector.

In concluding that we are not quite at the level of smart dust just yet, Prof. McCann showed off her most interesting example; a capsule that could be launched into space and then print out hundreds of 3 mm² sensor satellites. As the requirements may well change between lift-off and distribution, the printing could be remotely programmed from Earth to amend the specification of the satellites.

Bizarrely, Prof. McCann ended her talk with a clip from Adam Curtis’ anti-computer All Watched Over by Machines of Loving Grace, which prompted the best question of the session when Dr Mark Rouncefield asked if Prof. McCann knew what loving grace meant, before declaring that he [doesn’t] think anyone in this room knows what loving grace means and that we were all engineers who were just interested in building things. One can always rely on the Rounce to bring some colour to a technical talk.

Rather than aiming to build systems which replace human capabilities, we need to design systems that work in partnership with users.

Abigail Sellen, of The Myth of the Paperless Office fame, came to deliver an instalment of the SCC Distinguished Seminar Series on the process of symbiotic design whereby user behaviour is made intelligible to machines and machines are made intelligible to users. That final line cropped up a few times, and forms part of the most interesting takeaway of the seminar.

After a brief introduction (including a summary of the history of AI development and a comment about the Google Autocomplete results for artificial intelligence will… that elicited some chuckles), she launched into a case study of a project called Assess MS, produced by MSR in co-operation with Novartis International AG (that’s MS as in multiple sclerosis, not Microsoft). There’s a detailed writeup here, but in short she explained that the current MS diagnosis tool—the EDSS scale—is coarse-grained and subjective. A solution was desired that would be more consistent in its judgement of disease severity, as well as allow finer-grained ratings.

The first step, and tying in to the first idea of needing user behaviour [to be] made intelligible to machines, was ethnographic research into how neurologists currently conduct the tests. It became apparent that despite the standardisation of the test, there was still a lot of workflow variation between practitioners which produced a range of effects, e.g. getting the patient to perform a Romberg’s test before the finger-to-nose test can make them perform the latter ventrally, rather than laterally. This is no issue for a human neurologist, but throws up issues for a machine learning algorithm. She also mentioned the rather clever idea of putting the neurologist*rsquo;s screen on the back of the unit, encouraging them to stand behind it rather than blocking the camera.

Tying into the requirement that machines are made intelligible to users was the second half of the case study, focusing on how best to represent the data to the neurologists. After trying heatmaps and a number of other methods of data display, the consensus of preference was for the original videos as the most important tool for the neurologists to analyse, with the slew of other visualisations viewed as helpful tools to assist the expert diagnosis.

The second case study was less interesting — a consumer app that allows the user to take a photograph of objects with their phone camera and have the focus image segmented out and stored in a kind of object scrapbook. Users could then tag images and organise them into collections, with the standard Microsoft app demo video (with soft folksy backing music, natch) showing collections for things like action figures and Mary’s art. Seconds after I wrote in my notes that it was basically Pinterest, Sellen brought up that many people erroneously think the app is basically Pinterest. However, her argument that Pinterest is for collating existing web content whilst the app in question was for collating physical objects didn’t hold water for me — surely the web content on Pinterest was originally physical objects too? I don’t think the addition of image segmentation is a big enough difference, nor do I particularly like the thought of physical objects—that last bastion of sentiment and meaning—being reduced to content as ephemeral as a Facebook photo.

However, as someone who quite likes using social cataloguing sites (e.g. Goodreads) to keep track of different forms of media, I can see a possible appeal for collectors of things like action figures, especially with the addition of backdrops for objects, including podiums and a bookshelf. Sellen also suggested the real reason Microsoft is pursuing the project when she said that it generated a large, semantically meaningful dataset that can be used to train other machine learning algorithms within the company. There was also a great point by an anonymous user about the fallibility of the algorithms:

For example, I added my Princess Peach figurine today and the suggested tags were paper towel, diaper and teddy.

The third, shortest case study was even less convincing — a concept video for a Lord of the Rings Top Trumps app where you take a photo of a friend and the app determines which character they most resemble and then creates that card in your deck with their face superimposed over the characters. After that, it’s just standard Top Trumps. It looked pointless.

To round off, Sellen elaborated on the four steps that users must understand in order to fulfil the goal of design[ing] systems that work in partnership with users: sensing, or users understanding what a system is capable of seeing (e.g. the screen on the back of the Assess MS unit); perceiving, or users understanding how a system perceives forms and patterns in data (e.g. a shiny chair messing with a depth view whilst other clutter was safely ignored, whereas a human would ignore all the clutter); recognition, or users understanding how the system labels forms and patterns (e.g. the consumer app offering a suggested tag of hermit crab for a scrunched-up bit of paper that vaguely resembled one); and acting, or users understanding the rules by which the system responds to data, which engenders trust. The recognition part came with the aphorism that it doesn’t matter if the system isn’t always correct, as long as users can understand why it was wrong.

The followup questions got me thinking though. Dr Mark Rouncefield talked about how all the old critiques of AI have not been answered, they’ve merely been ignored and the focus of the field moved to that of human intelligence in said. Whilst by the end of it he had forgotten to ask a question, AI is dead, it died a long time ago was certainly a characteristically polemical point. Sellen riposted that it wasn’t dead, it had merely developed to using new techniques, such as neural nets. Another academic suggested the AI didn’t mimic human intelligence perfectly, and that was its strength — the machine finds connections that we don’t spot.

A final point was made from someone in the psychology department that evolutionary psychology models the human mind as a computer, and Sellen added that evolutionary biology and genetic algorithms do much the same blurring of disciplines. She concluded with the driving thesis of symbiotic computing — using machine learning to produce consistent judgement over similar cases, whilst keeping a human around for a creative element, e.g. an MS patient with one arm.

On the downside, she said paradigm shift a lot.