An Adaptive Modeling Framework for Bivariate Data Streams with Applications to Change Detection in Cyber-Physical Systems

In Cyber-Physical Systems (CPSs), physical processes, computational resources and communi- cation capabilities are tightly interconnected. Traditionally, the physical components of a CPS are described by means of differential or difference equations, while the cyber components are modeled by means of discrete dynamics. Therefore, hybrid systems, that are heterogeneous dynamical sys- tems characterized by the interaction of continuous and discrete dynamics, are a powerful modeling framework to deal with CPSs. Motivated by the great importance of security issues for CPSs, we characterize the observability and diagnosability properties for hybrid systems in the general case where the available information may be corrupted by an external attacker. Then, as CPSs are found in a wide range of applications, we demonstrate how to estimate the continuous state by simulating two scenarios: the control of a Direct Current (DC) Microgrid, and the control of a network of Unmanned Aerial Vehicles (UAVs) cooperatively transporting a payload.

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Continuous detection of concept drift in industrial cyber-physical systems using closed loop incremental machine learning

Discover Artificial Intelligence ◽

10.1007/s44163-021-00007-z ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Dinithi Jayaratne ◽

Daswin De Silva ◽

Damminda Alahakoon ◽

Xinghuo Yu

Keyword(s):

Machine Learning ◽

Data Streams ◽

Learning Algorithm ◽

Concept Drift ◽

Cyber Physical Systems ◽

Industrial Applications ◽

Physical Systems ◽

Concept Drift Detection ◽

Continuous Concept ◽

Time Critical

AbstractThe embedded, computational and cloud elements of industrial cyber physical systems (CPS) generate large volumes of data at high velocity to support the operations and functions of corresponding time-critical and mission-critical physical entities. Given the non-deterministic nature of these entities, the generated data streams are susceptible to dynamic and abrupt changes. Such changes, which are formally defined as concept drifts, leads to a decline in the accuracy and robustness of predicted CPS behaviors. Most existing work in concept drift detection are classifier dependent and require labeled data. However, CPS data streams are unlabeled, unstructured and change over time. In this paper, we propose an unsupervised machine learning algorithm for continuous concept drift detection in industrial CPS. This algorithm demonstrates three types of unsupervised learning, online, incremental and decremental. Furthermore, it distinguishes between abrupt and reoccurring drifts. We conducted experiments on SEA, a widely cited synthetic dataset of concept drift detection, and two industrial applications of CPS, task tracking in factory settings and smart energy consumption. The results of these experiments successfully validate the key features of the proposed algorithm and its utility of detecting change in non-deterministic CPS environments.

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