The use ofinvariantsin developing security mechanisms has become an attractive research area because of their potential to both prevent attacks and detect attacks in Cyber-Physical Systems (CPS). In general, an invariant is a property that is expressed using design parameters along with Boolean operators and which always holds in normal operation of a system, in particular, a CPS. Invariants can be derived by analysing operational data of various design parameters in a running CPS, or by analysing the system's requirements/design documents, with both of the approaches demonstrating significant potential to detect and prevent cyber-attacks on a CPS. While data-driven invariant generation can be fully automated, design-driven invariant generation has a substantial manual intervention. In this paper, we aim to highlight the shortcomings in data-driven invariants by demonstrating a set of adversarial attacks on such invariants. We propose a solution strategy to detect such attacks by complementing them with design-driven invariants. We perform all our experiments on a real water treatment testbed. We shall demonstrate that our approach can significantly reduce false positives and achieve high accuracy in attack detection on CPSs.

Abstract Cyber-physical systems (CPSs) in critical infrastructure face serious threats of attack, motivating research into a wide variety of defence mechanisms such as those that monitor for violations ofinvariants, i.e. logical properties over sensor and actuator states that should always be true. Many approaches for identifying invariants attempt to do so automatically, typically using data logs, but these can miss valid system properties if relevant behaviours are not well-represented in the data. Furthermore, as the CPS is already built, resolving any design flaws or weak points identified through this process is costly. In this paper, we propose a systematic method for deriving invariants from an analysis of a CPSdesign, based on principles of the axiomatic design methodology from design science. Our method iteratively decomposes a high-level CPS design to identify sets of dependentdesign parameters(i.e. sensors and actuators), allowing for invariants and invariant checkers to be derived in parallel to the implementation of the system. We apply our method to the designs of two CPS testbeds, SWaT and WADI, deriving a suite of invariant checkers that are able to detect a variety of single- and multi-stage attacks without any false positives. Finally, we reflect on the strengths and weaknesses of our approach, how it can be complemented by other defence mechanisms, and how it could help engineers to identify and resolve weak points in a design before the controllers of a CPS are implemented.

Although significant attention is given to the study of intellectual property rights (IPR) in economic and other literatures our understanding of the impact of these rights on the process of technological advance is surprisingly incomplete. In this paper we focus on one form of IPR, namely patents. An important and open question faced by policy-makers is what form of patent regime will encourage the fastest rate of technological progress in a society. It is difficult to address this question using historical empirical data as the legal, cultural and technological environments (to name but a few of the factors which could impact on the effect of a given patent regime) do not remain constant over time. Consequently, in this study we novelly employ an agent-based methodology in order to isolate and examine the rate of technological advance that different patent regimes produce. The simulation results indicate that, perhaps counter intuitively, patent policy may not in fact be an effective means of driving societal technological advance.

This study describes a novel simulation model of the process of product invention (InventSim). Invention is conceptualized as search on a landscape of product design possibilities, by a population of profit-seeking agents (inventors). The search process embeds both social and individual learning and is modelled using a memetic-type algorithm. The algorithm includes the search heuristics of thought experiments and election. This study examines the impact of these heuristics on the rate of invention in a society where inventors' estimates of the expected fitness (payoffs) of proposed inventions are noisy. The simulation results indicate that the heuristics are crucial in driving forward the inventive process even when payoff expectations are noisy.

This study describes a novel simulation model of the process of product invention. Invention is conceptualized as a process of directed evolutionary adaptation, on a landscape of product design possibilities, by a population of profit-seeking agents (inventors). The simulation experiments examine the sensitivity of the rate of advance in product fitness to the choice of search heuristics employed by inventors. The key finding of the experiments is that if search heuristics are confined to those which are rooted in past experience, or to heuristics which merely generate variety, limited product advance occurs. Notable product fitness advance only occurs when inventor's expectations as to the relative fitness of potential product inventions are incorporated into the model of invention. The results demonstrate the importance of human direction and expectations in invention. They also support the importance of formal product / project evaluation procedures in organizations, and the importance of market information when inventing new products.

The key task of corporate strategists is to uncover and implement viable strategies for their organization. This is a difficult task for several reasons, including uncertainty as to future payoffs, and strategic inertia. This study, using a swarm metaphor, constructs a simulation model to examine the impact of strategic inertia on the adaptation of the strategic fitness of a population of organizations. The results suggest that a degree of strategic inertia, in the presence of an election operator, can assist rather than hamper adaptive efforts of organizations in static and slowly changing environments.

To achieve a high degree of autonomy, an agent usually needs some kind of memory mechanism. We present a new approach to the evolution of agents with memory, based on the use of genetically programmed networks. These are connectionist structures where each node has an associated program, evolved using genetic programming. Genetically programmed networks can easily be evolved into agents with very different architectures. We present experimental results from evolving genetically programmed networks as neural networks, distributed programs and rule based systems capable of solving problems where the use of memory by the agent is essential. Comparisons are made between the performance of these solutions and the performance of solutions obtained by other evolutionary strategies used to evolve agents with memory.