Security intrusions as mechanisms

The practice of security often revolves around figuring out what (malicious act) happened to a system. This historical inquiry is the focus of forensics, specifically when the inquiry regards a policy violation (such as a law). The results of forensic investigation might be used to fix the impacted system, attribute the attack to adversaries, or build more resilient systems going forwards. However, to execute any of these purposes, the investigator first must discover the mechanism of the intrusion.

As discussed at an ACE seminar last October, one common framework for this discovery task is the intrusion kill chain. Mechanisms, mechanistic explanation, and mechanism discovery have highly-developed meanings in the biological and social sciences, but the word is not often used in information security. In a recent paper, we argue that incident response and forensics investigators would be well-served to make use of the existing literature on mechanisms, as thinking about intrusion kill chains as mechanisms is a productive and useful way to frame the work.

To some extent, thinking mechanistically is a description of what (certain) scientists do. But the mechanisms literature within philosophy of science is not merely descriptive. The normative benefits extolled include that thinking mechanistically is an effective heuristic for searching out useful explanations; mechanisms provide the most coherent unity to complex fields of study; and that mechanistic explanation is necessary to guide selection among potential studies given limited experimental resources, experiment design decisions, and interpretation of statistical results. I previously argued that capricious use of biological metaphors is bad for information security. We are keenly aware that these benefits of mechanistic explanation need to apply to security as and for security, not merely because they work in other sciences.

Our paper demonstrates how we can cast the intrusion kill chain, the diamond model, and other models of security intrusions as mechanistic models. This casting begins to demonstrate the mosaic unity of information security. Campaigns are made up of attacks. Attacks, as modeled by the kill chain, have multiple steps. In a specific attack, the delivery step might be accomplished by a drive-by-download. So we demonstrate how drive-by-downloads are a mechanism, one among many possible delivery mechanisms. This description is a schema to be filled in during a particular drive-by download incident with a specific URL and specific javascript, etc. The mechanistic schema of the delivery mechanism informs the investigator because it indicates what types of network addresses to look for, and how to fit them into the explanation quickly. This process is what Lindley Darden calls schema instantiation in the mechanism discovery literature.

Our argument is not that good forensics investigators do not do such mechanism discovery strategies. Rather, it is precisely that good investigators do do them. But we need to describe what it is good investigators in fact do. We do not currently, and that lack makes teaching new investigators particularly difficult. Thinking about intrusions as mechanisms unlocks an expansive literature on good ways to do mechanism discovery. This literature will make it easier to codify what good investigators do, which among other benefits allows us to better teach sound methodological practices to incoming investigators.

Our paper on this topic was published in the open-access Journal of Cybersecurity, as Thinking about intrusion kill chains as mechanisms, by Jonathan M. Spring and Eric Hatleback.

Category errors in (information) security: how logic can help

(Information) security can, pretty strongly arguably, be defined as being the process by which it is ensured that just the right agents have just the right access to just the right (information) resources at just the right time. Of course, one can refine this rather pithy definition somewhat, and apply tailored versions of it to one’s favourite applications and scenarios.

A convenient taxonomy for information security is determined by the concepts of confidentiality, integrity, and availability, or CIA; informally:

the property that just the right agents have access to specified information or systems;
the property that specified information or systems are as they should be;
the property that specified information or systems can be accessed or used when required.

Alternatives to confidentiality, integrity, and availability are sensitivity and criticality, in which sensitivity amounts to confidentiality together with some aspects of integrity and criticality amounts to availability together with some aspects of integrity.

But the key point about these categories of phenomena is that they are declarative; that is, they provide a statement of what is required. For example, that all documents marked ‘company private’ be accessible only to the company’s employees (confidentiality), or that all passengers on the aircraft be free of weapons (integrity), or that the company’s servers be up and running 99.99% of the time (availability).

It’s all very well stating, declaratively, one’s security objectives, but how are they to be achieved? Declarative concepts should not be confused with operational concepts; that is, ones that describe how something is done. For example, passwords and encryption are used to ensure that documents remain confidential, or security searches ensure that passengers do not carry weapons onto an aircraft, or RAID servers are employed to ensure adequate system availability. So, along with each declarative aim there is a collection of operational tools that can be used to achieve it.

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Mathematical Modelling in the Two Cultures

Models, mostly based on mathematics of one kind or another, are used everywhere to help organizations make decisions about their design, policies, investment, and operations. They are indispensable.

But if modelling is such a great idea, and such a great help, why do so many things go wrong? Well, there’s good modelling and less good modelling. And it’s hard for the consumers of models — in companies, the Civil Service, government agencies — to know when they’re getting the good stuff. Worse, there’s a lot of comment and advice out there which at best doesn’t help, and perhaps makes things worse.

In 1959, the celebrated scientist and novelist C. P. Snow delivered the Rede Lecture on ‘The Two Cultures’. Snow later published a book developing the ideas as ‘The Two Cultures and the Scientific Revolution’.

A famous passage from Snow’s lecture is the following (it can be found in Wikipedia):

‘A good many times I have been present at gatherings of people who, by the standards of the traditional culture, are thought highly educated and who have with considerable gusto been expressing their incredulity at the illiteracy of scientists. Once or twice I have been provoked and have asked the company how many of them could describe the Second Law of Thermodynamics. The response was cold: it was also negative. Yet I was asking something which is the scientific equivalent of: Have you read a work of Shakespeare’s?

‘I now believe that if I had asked an even simpler question — such as, What do you mean by mass, or acceleration, which is the scientific equivalent of saying, Can you read? — not more than one in ten of the highly educated would have felt that I was speaking the same language. So the great edifice of modern physics goes up, and the majority of the cleverest people in the western world have about as much insight into it as their neolithic ancestors would have had.’

Over the decades since, society has come to depend upon mathematics, and on mathematical models in particular, to a very great extent. Alas, the mathematical sophistication of the great majority of consumers of models has not really improved. Perhaps it has even deteriorated.

So, as mathematicians and modellers, we need to make things work. The starting point for good modelling is communication with the client.

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