A New Systems Model for Human-Centered Security Analysis Based on Modes and Mode Transitions

Core Concepts

A new conceptual framework for analyzing complex security systems using the notions of modes and mode transitions, where a mode is an independent component with its own objectives, monitoring data, algorithms, and scope of action, and mode transitions are determined by interpretations of monitoring data and capabilities in light of objectives.

Abstract

The paper proposes a new conceptual framework for analyzing complex security systems using the notions of modes and mode transitions. A mode is defined as an independent component of the system with its own objectives, monitoring data, algorithms, and scope of action. The behavior of a mode, including its transitions to other modes, is determined by interpretations of the mode's monitoring data and capabilities in light of its objectives, which are termed beliefs. The framework is formalized mathematically using simplicial complexes to visualize the beliefs and mode transitions. Three security scenarios are used to demonstrate the application of the framework: Triage for classifying "persons of interest" based on available evidence. Mapping the potential causes and effects of a cyber security incident. Examining a multi-agency response to a critical incident using the UK Gold-Silver-Bronze command structure. The key principles of the framework include completeness (a system can be in one or more modes at a time), composition (joint modes can be formed by combining other modes), localization (each mode has its own monitoring data and evidence space), globalization (the overall system state is a synthesis of the modes' evidence), quantification (the relevance of modes to the system state is quantified), and visualization (the beliefs and mode transitions are represented geometrically using simplicial complexes). The framework aims to provide a transparent and explainable approach to designing, analyzing, and understanding complex security systems, especially in human-centered contexts.

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On human-centred security: A new systems model based on modes and mode transitions

by Edwin J Begg... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.02043.pdf

Deeper Inquiries

How can the proposed framework be extended to handle uncertainty and incomplete information in security scenarios?

The proposed framework can be extended to handle uncertainty and incomplete information in security scenarios by incorporating probabilistic models and belief functions. By utilizing techniques from uncertainty quantification and Bayesian inference, the framework can assign probabilities to different modes and mode transitions based on available evidence. This allows for a more nuanced understanding of the system's behavior in the face of uncertainty. Additionally, the framework can incorporate fuzzy logic to handle vague or imprecise information, providing a more flexible approach to decision-making in security scenarios where information may be incomplete or ambiguous.

What are the potential limitations and challenges in applying the simplicial complex-based visualization and quantification approach to real-world security systems?

One potential limitation of applying the simplicial complex-based visualization and quantification approach to real-world security systems is the complexity of modeling real-world systems accurately. Security systems often involve a large number of variables and interactions, which may not be easily captured in a geometric representation. Additionally, the scalability of the approach may be a challenge when dealing with large and dynamic security systems. Ensuring the accuracy and reliability of the belief functions used for quantification is another challenge, as incorrect beliefs can lead to suboptimal decision-making in security scenarios. Furthermore, the interpretation of geometric visualizations may require specialized training, posing a challenge for widespread adoption in security settings.

How can the framework be integrated with machine learning and other AI techniques to enhance the automation and decision-making capabilities of security systems?

The framework can be integrated with machine learning and other AI techniques to enhance the automation and decision-making capabilities of security systems by leveraging the power of data-driven algorithms. Machine learning algorithms can be used to analyze large datasets and identify patterns or anomalies that may indicate security threats. By incorporating machine learning models into the framework, security systems can automate the process of monitoring, detecting, and responding to security incidents in real-time. Additionally, reinforcement learning techniques can be employed to optimize decision-making processes within the framework, allowing security systems to adapt and improve their responses over time. Overall, the integration of AI techniques can enhance the efficiency and effectiveness of security systems in mitigating risks and ensuring the safety of critical assets.