Recent advances in pervasive computing have caused a rapid growth of the Smart Home market, where a number of otherwise mundane pieces of technology are capable of connecting to the Internet and interacting with other similar devices. However, with the lack of a commonly adopted set of guidelines, several IT companies are producing smart devices with their own proprietary standards, leading to highly heterogeneous Smart Home systems in which the interoperability of the present elements is not always implemented in the most straightforward manner. As such, understanding the cyber risk of these cyber-physical systems beyond the individual devices has become an almost intractable problem. This paper tackles this issue by introducing a Smart Home reference architecture which facilitates security analysis. Being composed by three viewpoints, it gives a high-level description of the various functions and components needed in a domestic IoT device and network. Furthermore, this document demonstrates how the architecture can be used to determine the various attack surfaces of a home automation system from which its key vulnerabilities can be determined.

Uncertainties in machine learning are a significant roadblock for its application in safety-critical cyber-physical systems (CPS). One source of uncertainty arises from distribution shifts in the input data between training and test scenarios. Detecting such distribution shifts in real-time is an emerging approach to address the challenge. The high dimensional input space in CPS applications involving imaging adds extra difficulty to the task. Generative learning models are widely adopted for the task, namely out-of-distribution (OoD) detection. To improve the state-of-the-art, we studied existing proposals from both machine learning and CPS fields. In the latter, safety monitoring in real-time for autonomous driving agents has been a focus. Exploiting the spatiotemporal correlation of motion in videos, we can robustly detect hazardous motion around autonomous driving agents. Inspired by the latest advances in the Variational Autoencoder (VAE) theory and practice, we tapped into the prior knowledge in data to further boost OoD detection’s robustness. Comparison studies over nuScenes and Synthia data sets show our methods significantly improve detection capabilities of OoD factors unique to driving scenarios, 42% better than state-of-the-art approaches. Our model also generalized near-perfectly, 97% better than the state-of-the-art across the real-world and simulation driving data sets experimented. Finally, we customized one proposed method into a twin-encoder model that can be deployed to resource limited embedded devices for real-time OoD detection. Its execution time was reduced over four times in low-precision 8-bit integer inference, while detection capability is comparable to its corresponding floating-point model.

Machine learning (ML) is actively finding its way into modern cyber-physical systems (CPS), many of which are safety-critical real-time systems. It is well known that ML outputs are not reliable when testing data are novel with regards to model training and validation data, i.e., out-of-distribution (OOD) test data. We implement an unsupervised deep neural network-based OOD detector on a real-time embedded autonomous Duckiebot and evaluate detection performance. Our OOD detector produces a success rate of 87.5% for emergency stopping a Duckiebot on a braking test bed we designed. We also provide case analysis on computing resource challenges specific to the Robot Operating System (ROS) middleware on the Duckiebot.

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As the industrial cyber-infrastructure become increasingly important to realise the objectives of Industry 4.0, the consequence of disruption due to internal or external faults become increasingly severe. Thus there is a need for a resilient infrastructure. In this paper, we propose a contractbased methodology where components across layers of the cyberinfrastructure are associated with contracts and a light-weight resilience manager. This allows the system to detect faults (contract violation monitored using observers) and react (change contracts dynamically) effectively.