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1. Probabilistic Modeling of Information Dynamics in Networked Cyber-Physical-Social Systems

   https://doi.org/10.1109/JIOT.2021.3072893  

   Wang, Yan (April 2021, IEEE Internet of Things Journal)

   null (Ed.)

   Cyber-physical-social systems (CPSS) are physical devices with highly integrated functions of sensing, computing, communication and control, and are seamlessly embedded in human society. The levels of intelligence and functions that CPSS can perform rely on their extensive collaboration and information sharing through networks. In this paper, information diffusion within CPSS networks is studied. Information dynamics models are proposed to characterize the evolution of information processing and decision making capabilities of individual CPSS nodes. The data-driven statistical models are based on a mesoscale probabilistic graph model, where the individual nodes' sensing and computing functions are represented as the probabilities of correct predictions, whereas the communication functions are represented as the probabilities of mutual influences between nodes. A copula dynamics model is proposed to explicitly capture the information dependency among individuals with joint prediction probabilities and estimated from extremal probabilities. A topology-informed vector autoregression model is proposed to represent the mutual influence of prediction capabilities. A spatial-temporal hybrid Gaussian process regression model is also proposed to simultaneously capture correlations between nodes and temporal correlation in the time series. 

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2. Topology-informed information dynamics modeling in cyber–physical–social system networks

   https://doi.org/10.1017/S0890060421000159  

   Wang, Yan (July 2021, Artificial Intelligence for Engineering Design, Analysis and Manufacturing)

   null (Ed.)

   Cyber–physical–social systems (CPSS) are physical devices that are embedded in human society and possess highly integrated functionalities of sensing, computing, communication, and control. CPSS rely on their intense collaboration and information sharing through networks to be functioning. In this paper, topology-informed network information dynamics models are proposed to characterize the evolution of information processing capabilities of CPSS nodes in networks. The models are based on a mesoscale probabilistic graph model, where the sensing and computing capabilities of the nodes are captured as the probabilities of correct predictions. A topology-informed vector autoregression model and a latent variable vector autoregression model are proposed to model the correlations between prediction capabilities of nodes as linear functional relationships. A hybrid Gaussian process regression model is also developed to capture both the nonlinear spatial and temporal correlations between nodes. The new information dynamics models are demonstrated and tested with a simulator of CPSS networks. The results show that the topological information of networks can improve the efficiency in constructing the time series models. The network topology also has influences on the prediction capabilities of CPSS. 

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3. Trustworthiness in designing cyber-physical systems

   Wang, Yan (May 2018, Proceedings of 12th International Symposium on Tools and Methods of Competitive Engineering (TMCE2018))

   Cyber-physical systems (CPS) provide unique functions of data collection, processing, communication, and control. The advanced capabilities and functions of CPS rely on their highly networked working environment and deep interdependency. The effectiveness of their performance critically depends on what and how they share among each other. Designing a trustworthy network that CPS can work together collaboratively thus is important. In order to design trustable CPS products, quantitative measures of trustworthiness are required. In this paper, quantitative metrics of trustworthiness, including capability, benevolence, and integrity, are proposed based on a new probabilistic graph model. The proposed metrics can be calculated from either subjective perception or objective information of network topology. A design optimization framework based on the trustworthiness metrics is also demonstrated. 

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4. Hash-MAC-DSDV: Mutual Authentication for Intelligent IoT-Based Cyber-Physical Systems

   https://doi.org/10.1109/jiot.2021.3083731  

   Adil, Muhammad; Jan, Mian Ahmad; Mastorakis, Spyridon; Song, Houbing; Jadoon, Muhammad Mohsin; Abbas, Safia; Farouk, Ahmed (September 2021, IEEE Internet of Things Journal)

   null (Ed.)

   Cyber-Physical Systems (CPS) connected in the form of Internet of Things (IoT) are vulnerable to various security threats, due to the infrastructure-less deployment of IoT devices. Device-to-Device (D2D) authentication of these networks ensures the integrity, authenticity, and confidentiality of information in the deployed area. The literature suggests different approaches to address security issues in CPS technologies. However, they are mostly based on centralized techniques or specific system deployments with higher cost of computation and communication. It is therefore necessary to develop an effective scheme that can resolve the security problems in CPS technologies of IoT devices. In this paper, a lightweight Hash-MAC-DSDV (Hash Media Access Control Destination Sequence Distance Vector) routing scheme is proposed to resolve authentication issues in CPS technologies, connected in the form of IoT networks. For this purpose, a CPS of IoT devices (multi-WSNs) is developed from the local-chain and public chain, respectively. The proposed scheme ensures D2D authentication by the Hash-MAC-DSDV mutual scheme, where the MAC addresses of individual devices are registered in the first phase and advertised in the network in the second phase. The proposed scheme allows legitimate devices to modify their routing table and unicast the one-way hash authentication mechanism to transfer their captured data from source towards the destination. Our evaluation results demonstrate that Hash-MAC-DSDV outweighs the existing schemes in terms of attack detection, energy consumption and communication metrics. 

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5. Digital Twin Based Asynchronous Federated Learning Enabled IDS for False Data Injection Attacks in Vehicular CPS

   https://doi.org/10.1109/ICCSM63823.2024.00012  

   Safavat, Sunitha; Rawat, Danda B (July 2024, IEEE)

   Cyber Physical Systems (CPS) consist of integration of cyber and physical spaces through computing, communication, and control operations. In vehicular CPS, modern vehicles with multiple Electronic Control Units (ECUs) and networking with other vehicles help autonomous driving. Vehicular CPS is vulner-able to multitude of cyber attacks, including false data injection attacks. This paper presents an Asynchronous Federated Learning (AFL) with a Gated Recurrent Unit (GRU) model for identifying False Data Injection (FDI) attacks in a VCPS. The AFL model continuously monitors the network and constructs a digital twin using the data obtained from a VCPS for intrusion detection. The proposed model is evaluated using different evaluation metrics. Numerical results show that the AFL model outperforms other existing models. 

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