Epistemological Equation for Analysing Uncontrollable States in Complex
Systems: Quantifying Cyber Risks from the Internet of Things
- URL: http://arxiv.org/abs/2212.08141v1
- Date: Thu, 15 Dec 2022 21:02:49 GMT
- Title: Epistemological Equation for Analysing Uncontrollable States in Complex
Systems: Quantifying Cyber Risks from the Internet of Things
- Authors: Petar Radanliev, David De Roure, Pete Burnap and Omar Santos
- Abstract summary: The new analysis approach enables the assessment of uncontrollable risk states in complex IoT systems.
It can be used for a quantitative self-assessment of IoT cyber risk posture.
- Score: 3.1351527202068445
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: To enable quantitative risk assessment of uncontrollable risk states in
complex and coupled IoT systems, a new epistemological equation is designed and
tested though comparative and empirical analysis. The comparative analysis is
conducted on national digital strategies, followed by an empirical analysis of
cyber risk assessment approaches. The new epistemological analysis approach
enables the assessment of uncontrollable risk states in complex IoT systems,
which begin to resemble artificial intelligence, and can be used for a
quantitative self-assessment of IoT cyber risk posture.
Related papers
- From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems [2.226040060318401]
We translate System Theoretic Process Analysis (STPA) for analyzing AI operation and development processes.
We focus on systems that rely on machine learning algorithms and conductedA on three case studies.
We find that key concepts and steps of conducting anA readily apply, albeit with a few adaptations tailored for AI systems.
arXiv Detail & Related papers (2024-10-29T20:43:18Z) - Risks and NLP Design: A Case Study on Procedural Document QA [52.557503571760215]
We argue that clearer assessments of risks and harms to users will be possible when we specialize the analysis to more concrete applications and their plausible users.
We conduct a risk-oriented error analysis that could then inform the design of a future system to be deployed with lower risk of harm and better performance.
arXiv Detail & Related papers (2024-08-16T17:23:43Z) - EAIRiskBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [47.69642609574771]
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.
Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.
However, the deployment of these agents in physical environments presents significant safety challenges.
This study introduces EAIRiskBench, a novel framework for automated physical risk assessment in EAI scenarios.
arXiv Detail & Related papers (2024-08-08T13:19:37Z) - Asset-centric Threat Modeling for AI-based Systems [7.696807063718328]
This paper presents ThreatFinderAI, an approach and tool to model AI-related assets, threats, countermeasures, and quantify residual risks.
To evaluate the practicality of the approach, participants were tasked to recreate a threat model developed by cybersecurity experts of an AI-based healthcare platform.
Overall, the solution's usability was well-perceived and effectively supports threat identification and risk discussion.
arXiv Detail & Related papers (2024-03-11T08:40:01Z) - A Cybersecurity Risk Analysis Framework for Systems with Artificial
Intelligence Components [0.0]
The introduction of the European Union Artificial Intelligence Act, the NIST Artificial Intelligence Risk Management Framework, and related norms demands a better understanding and implementation of novel risk analysis approaches to evaluate systems with Artificial Intelligence components.
This paper provides a cybersecurity risk analysis framework that can help assessing such systems.
arXiv Detail & Related papers (2024-01-03T09:06:39Z) - It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation [50.06412862964449]
Attack Graph (AG) represents the best-suited solution to support cyber risk assessment for multi-step attacks on computer networks.
Current solutions propose to address the generation problem from the algorithmic perspective and postulate the analysis only after the generation is complete.
This paper rethinks the classic AG analysis through a novel workflow in which the analyst can query the system anytime.
arXiv Detail & Related papers (2023-12-27T10:44:58Z) - Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks [142.67349734180445]
Existing algorithms that provide risk-awareness to deep neural networks are complex and ad-hoc.
Here we present capsa, a framework for extending models with risk-awareness.
arXiv Detail & Related papers (2023-08-01T02:07:47Z) - Leveraging Traceability to Integrate Safety Analysis Artifacts into the
Software Development Process [51.42800587382228]
Safety assurance cases (SACs) can be challenging to maintain during system evolution.
We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models.
We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
arXiv Detail & Related papers (2023-07-14T16:03:27Z) - System Safety Engineering for Social and Ethical ML Risks: A Case Study [0.5249805590164902]
Governments, industry, and academia have undertaken efforts to identify and mitigate harms in ML-driven systems.
Existing approaches are largely disjointed, ad-hoc and of unknown effectiveness.
We focus in particular on how this analysis can extend to identifying social and ethical risks and developing concrete design-level controls to mitigate them.
arXiv Detail & Related papers (2022-11-08T22:58:58Z) - Quantitative AI Risk Assessments: Opportunities and Challenges [9.262092738841979]
AI-based systems are increasingly being leveraged to provide value to organizations, individuals, and society.
Risks have led to proposed regulations, litigation, and general societal concerns.
This paper explores the concept of a quantitative AI Risk Assessment.
arXiv Detail & Related papers (2022-09-13T21:47:25Z) - SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics.
We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations.
We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
arXiv Detail & Related papers (2020-06-12T10:40:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.