Improving the robustness of industrial Cyber-Physical Systems through machine learning-based performance anomaly identification

We propose a versatile and fully data-centric methodology towards anomaly detection and identification in modern industrial Cyber-Physical Systems (CPS). Our motivation behind this move is the ever-growing computerisation in these systems, in the form of complex distributed computing nodes, running complex distributed software. Industrial CPS also demonstrate heavy deployment of hardware sensors, as well as an increasing role for software. We observe the insufficiency and costliness of design-time measures in prevention of anomalies. As our main contribution, our methodology is taking advantage of this data-rich environment by means of Extra-Functional Behaviour (EFB) monitoring, analytics pipelines and Artificial Intelligence (AI). Specifically, we demonstrate the use of compartmentalisation of execution timelines into distinct units, i.e., execution phases. We introduce the generation of representations for these phases, i.e., behavioural signatures and behavioural passports, as our way of behavioural fingerprinting. Composed using regression modelling techniques, signatures as the representation of ongoing behaviour, are compared to passports, representing reference behaviour. The comparison is done by means of goodness-of-fit scores, creating quantifiable measures of deviation between different recorded behaviours. We have used both partially synthetic and real-world traces in our experiments, depending on the use-case. We have also followed both white box and black box approaches for our use-cases, with discussions on the pros and cons of each.The effectiveness of our data-centric methodology is demonstrated by two proofs-of-concept from the industry, to represent the two ends of the industrial CPS complexity spectrum, with one being a large semiconductor photolithography machine, while the other is an image analysis platform. Each use-case comes with its own characteristics and limitations, confirming the flexibility of our methodology and the relevance of its integral steps in the approach towards the initial analysis and data transformations. The results of anomaly classification show overall high accuracies, as high as 99% in certain set-ups. These results show the capability of our data-centric methodology, suiting the presented modern industrial CPS designs.