Co-designing heterogeneous models: a distributed systems approach
July 10, 2024 Β· Declared Dead Β· π Journal of Cybersecurity
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Marius-Constantin Ilau, Tristan Caulfield, David Pym
arXiv ID
2407.07656
Category
cs.SE: Software Engineering
Cross-listed
cs.CR
Citations
0
Venue
Journal of Cybersecurity
Last Checked
5 months ago
Abstract
The nature of information security has been, and probably will continue to be, marked by the asymmetric competition of attackers and defenders over the control of an uncertain environment. The reduction of this degree of uncertainty via an increase in understanding of that environment is a primary objective for both sides. Models are useful tools in this context because they provide a way to understand and experiment with their targets without the usual operational constraints. However, given the technological and social advancements of today, the object of modelling has increased in complexity. Such objects are no longer singular entities, but heterogeneous socio-technical systems interlinked to form large-scale ecosystems. Furthermore, the underlying components of a system might be based on very different epistemic assumptions and methodologies for construction and use. Naturally, consistent, rigorous reasoning about such systems is hard, but necessary for achieving both security and resilience. The goal of this paper is to present a modelling approach tailored for heterogeneous systems based on three elements: an inferentialist interpretation of what a model is, a distributed systems metaphor to structure that interpretation and a co-design cycle to describe the practical design and construction of the model. The underlying idea is that an open world interpretation, supported by a formal, yet generic abstraction facilitating knowledge translation and providing properties for structured reasoning and, used in practice according to the co-design cycle could lead to models that are more likely to achieve their pre-stated goals. We explore the suitability of this method in the context of three different security-oriented models: a physical data loss model, an organisational recovery under ransomware model and an surge capacity trauma unit model.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted