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Research Topic Description, including Problem Statement:
Sensors/scanners are required by security and defense stakeholders for the detection and identification of threat and prohibited items
As edge computing increases support for AI and machine learning in network devices, we expect future cyber-physical systems to grow in complexity and autonomy, and to work in collectives to achieve the scale required for large-scale cyber-ecosystems (such as in smart buildings, connected places, transport/logistics networks, energy distribution, and other critical national infrastructure). This could result in swarm engineering, or the use of Popperian intelligent agents having an internal model (or representation) of their situation, performance and attainment towards a goal. In either case, there is the potential for new classes of cyber vulnerability based on coercion or deception by an attacker. In addition, we forecast that large-scale collectives of cyber physical systems will increasingly behave as complex systems (CS), or complex adaptive systems (CAS) if learning and/or feedback is present. This too creates new classes of cyber-vulnerability, through the existence of level-points, phase transitions, bifurcation phenomena, percolation phenomena, and other system-wide phenomena new to us. These possibilities therefore need to be understood and prepared for.
A state-based approach has been suggested (Bramson, 2019 [1]) – in which the status and dynamics of the CAS can be represented in the form of a graph.
Figure 1: Graph showing attractor, A1, its basin of attraction (blue), and its support (yellow); and alternative attractor S33 with its basin of attraction (green) (reproduced from Bramson, 2019 [1] p96)
This topic is to establish a theoretical baseline for describing complex adaptive system states and relating this to properties, such as cyber-resilience and antifragility. The challenge includes:
Data will necessarily be synthetic, or restricted domain real-world, for this study as real-world wide area data is not yet available (and probably won’t be for some time). We anticipate the need for extensive computer simulation (based on trials and observations) to obtain some of the basic values and identifications. The need for training of key system parameters may require the identification of new machine learning techniques.
Ref: [1] Ted Carmichael (Editor), Andrew J. Collins (Editor), Mirsad Hadžikadic (Editor), Complex Adaptive Systems: Views from the Physical, Natural, and Social Sciences (Understanding Complex Systems), Springer; 1st ed. 2019 edition (27 Jun. 2019), ISBN-13: 978-3030203078
Example Approaches:
Complex System science is a development of General Systems Theory (1968) and some of the principles of Cybernetics (late 1940s), however, it remains immature and incomplete despite this pedigree. Although adequate explanations of Complex Adaptive Systems (CAS) remain elusive, techniques that are available include:
A major challenge in dealing with CAS is determining which of the possible approaches to modelling the various characteristics and phenomena are appropriate, and how we can reach a realistic model (even if at low resolution) that is useful.
Relevance to the Intelligence Community:
The IC needs to understand the threats and opportunities from Complex Systems and Complex Adaptive Systems before these architectures are widely deployed.
Key Words: Cybersecurity, Complex
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