Exploring a Mechanistic Approach to Experimentation in Computing

Hatleback, Eric; Spring, Jonathan M.

doi:10.1007/s13347-014-0164-9

Cited by 15 publications

(16 citation statements)

References 26 publications

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“…However, in other parts of the field, computer science methods are distinctly developed within the discipline. Even so, Hatleback and Spring (2014) argue that experiments and model-building in computing are not so different from other sciences, after accounting for the unique challenges of the fields. Separation Logic provides a good example of this second type; the above examples of temporal logic and shared memory consistency indicate it is not alone.…”

Section: Resultsmentioning

confidence: 99%

“…Separation Logic provides a good example of this second type; the above examples of temporal logic and shared memory consistency indicate it is not alone. Hatleback and Spring (2014) argue that reasoning about objects that can purposefully change at the same pace as the observer can interact with them, namely software, is a particular challenge in computer science. Separation Logic is an example of how computer scientists overcome this problem.…”

Section: Resultsmentioning

confidence: 99%

“…Discussions of such questions are available centering on general methodological views (Schiaffonati and Verdicchio 2014) or a mechanistic view (Hatleback and Spring 2014). The case of Separation Logic also might inform discussions of how to overcome challenges in 'science of security', especially in the area of the relationship between science, engineering and formalism .…”

Section: Introductionmentioning

confidence: 99%

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Why Separation Logic Works

2018

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One might poetically muse that computers have the essence both of logic and machines. Through the case of the history of Separation Logic, we explore how this assertion is more than idle poetry. Separation Logic works because it merges the software engineer's conceptual model of a program's manipulation of computer memory with the logical model that interprets what sentences in the logic are true, and because it has a proof theory which aids in the crucial problem of scaling the reasoning task. Scalability is a central problem, and some would even say the central problem, in applications of logic in computer science. Separation Logic is an interesting case because of its widespread success in verification tools. For these two senses of model-the engineering/conceptual and the logical-to merge in a genuine sense, each must maintain their norms of use from their home disciplines. When this occurs, both the logic and engineering benefit greatly. Seeking this intersection of two different senses of model provides a strategy for how computer scientists and logicians may be successful. Furthermore, the history of Separation Logic for analysing programs provides a novel case for philosophers of science of how software engineers and computer scientists develop models and the components of such models. We provide three contributions: an exploration of the extent of models merging that is necessary for success in computer science; an introduction to the technical details of Separation Logic, which can be used for reasoning about other exhaustible resources; and an introduction to (a subset of) the problems, process, and results of computer scientists for those outside the field.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Why Separation Logic Works

2018

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“…However, malware authors tend to leverage the ambiguity afforded by the dynamic nature of the software environment to their advantage. The salient aspect in which software is changeable is that argued by Hatleback and Spring (2014); not that the source code is editable, but that the behavior is both dynamic and designed to be responsive to the environment in arbitrary ways. Both the arbitrariness and having-been-designed make studying the dynamism of software a distinct challenge from dynamism in other fields such as chemistry and biology.…”

Section: The Three Challenges For Information Securitymentioning

confidence: 99%

Building General Knowledge of Mechanisms in Information Security

Spring

Illari

2018

Philos. Technol.

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We show how more general knowledge can be built in information security, by the building of knowledge of mechanism clusters, some of which are multifield. By doing this, we address in a novel way the longstanding philosophical problem of how, if at all, we come to have knowledge that is in any way general, when we seem to be confined to particular experiences. We also address the issue of building knowledge of mechanisms by studying an area that is new to the mechanisms literature: the methods of what we shall call mechanism discovery in information security. This domain offers a fascinating novel constellation of challenges for building more general knowledge. Specifically, the building of stable communicable mechanistic knowledge is impeded by the inherent changeability of software, which is deployed by malicious actors constantly changing how their software attacks, and also by an ineliminable secrecy concerning the details of attacks not just by attackers (black hats), but also by information security defenders (white hats) as they protect their methods from both attackers and commercial competitors. We draw out ideas from the work of the mechanists Darden, Craver, and Glennan to yield an approach to how general knowledge of mechanisms can be painstakingly built. We then use three related examples of active research problems from information security (botnets, computer network attacks, and malware analysis) to develop philosophical thinking about building general knowledge using mechanisms, and also apply this to develop insights for information security. We show that further study would be instructive both for practitioners (who might welcome the help in conceptualizing what they do) and for philosophers (who will find novel insights into building general knowledge of a highly changeable domain that has been neglected within philosophy of science).

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“…In doing so, some simplifications are necessary; otherwise the model becomes as useless as a 1:1 map. Models involving engineered mechanisms such as computer systems are difficult due to their dynamic and complex nature [3]. The Adversarial Capability Chain (ACC) model is coarse grained, so some stark simplifications must be made.…”

Section: Motivationmentioning

confidence: 99%

Global adversarial capability modeling

Spring

Kern

Summers

2015

2015 APWG Symposium on Electronic Crime Research (eCrime)

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Intro: Computer network defense has models for attacks and incidents comprised of multiple attacks after the fact. However, we lack an evidence-based model the likelihood and intensity of attacks and incidents.Purpose: We propose a model of global capability advancement, the adversarial capability chain (ACC), to fit this need. The model enables cyber risk analysis to better understand the costs for an adversary to attack a system, which directly influences the cost to defend it. Method: The model is based on four historical studies of adversarial capabilities: capability to exploit Windows XP, to exploit the Android API, to exploit Apache, and to administer compromised industrial control systems.Result: We propose the ACC with five phases: Discovery, Validation, Escalation, Democratization, and Ubiquity. We use the four case studies as examples as to how the ACC can be applied and used to predict attack likelihood and intensity.

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Exploring a Mechanistic Approach to Experimentation in Computing

Cited by 15 publications

References 26 publications

Why Separation Logic Works

Why Separation Logic Works

Building General Knowledge of Mechanisms in Information Security

Global adversarial capability modeling

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