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1. Smart City Concept Based on Cyber-Physical Social Systems with Hierarchical Ethical Agents Approach

   https://doi.org/10.1007/978-3-030-78095-1_31  

   Mata, O. (July 2021, 23rd International Conference on Human-Computer Interaction (HCII 2021) - Universal Access in Human-Computer Interaction. Access to Media, Learning and Assistive Environments)

   A smart city is considered a sustainable city that manages needed resources and makes autonomous decisions to improve the quality of life of its citizens. On the other hand, Cyber-Physical Systems (CPS) have been implemented as isolated systems inside the city. For instance, the traffic lights, autonomous navigation for cars, and so on. Instead, consider a smart city with an integrated CPS for independent blocks that can be interconnected in a central unit. However, when a CPS makes decisions about the integration of ethical concepts based on human perception, social space must be added, and so a CPS must be transformed into a Cyber-Physical Social System (CPSS). Furthermore, a new type of social interaction between all the elements in a CPSS within a smart city presents human behavioral challenges such as virtual-morality. This paper first proposes an Artificial Moral Agent with machine learning algorithms to regulate the interaction within the CPSS, adding itself to all the subsystems’ communication. Additionally, a moral agent structure is proposed with a morality filter as its fundamental component. 

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2. 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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3. On the machine learning for minimizing the negative influence in mobile cyber physical systems

   https://doi.org/10.1117/12.2519598  

   Chaudhary, Vijay; Rawat, Danda (May 2019, SPIE Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications)

   Emerging cyber physical system (CPS) are expected to enhance the overall performance of the networked systems to provide reliable services and applications to their users. However, massive number of connectivities in CPS bring security vulnerabilities and the mobility adds more complexity for securing the mobile CPS. Any mobile CPS can be represented as a graph with connectivity as well as with interactions among a group of mobile CPS nodes that plays a major role as a medium for the propagation of wrong/right information, and influence its members in the mobile CPS. This problem has wide spread applications in viral information disseminating in mobile CPS, where a malicious mobile CPS node may wish to spread the rumor via the most influential individuals in mobile CPS. In this paper, we design, develop and evaluate a machine learning approach that is based on a set theoretic approach for optimizing the influence in mobile CPS. This problem has applications in civilian and military systems. 

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4. Security engineering with machine learning for adversarial resiliency in cyber physical systems

   https://doi.org/10.1117/12.2519372  

   Olowononi, Felix; Rawat, Danda; Garuba, Moses; Kamhoua, Charles (May 2019, SPIE Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications)

   Recent technological advances provide the opportunities to bridge the physical world with cyber-space that leads to complex and multi-domain cyber physical systems (CPS) where physical systems are monitored and controlled using numerous smart sensors and cyber space to respond in real-time based on their operating environment. However, the rapid adoption of smart, adaptive and remotely accessible connected devices in CPS makes the cyberspace more complex and diverse as well as more vulnerable to multitude of cyber-attacks and adversaries. In this paper, we aim to design, develop and evaluate a distributed machine learning algorithm for adversarial resiliency where developed algorithm is expected to provide security in adversarial environment for critical mobile CPS. 

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5. Fingerprinting ECUs to Implement Vehicular Security for Passenger Safety Using Machine Learning Techniques

   https://doi.org/10.1007/978-3-031-16075-2_2  

   Bellaire, Samuel; Bayer, Matthew; Hafeez, Azeem; Refat, Rafi Ud; Malik, Hafiz (September 2022, Intelligence Systems (IntelliSys'22))

   The Controller Area Network (CAN) protocol used in vehicles today was designed to be fast, reliable, and robust. However, it is inherently insecure due to its lack of any kind of message authentication. Despite this, CAN is still used extensively in the automotive industry for various electronic control units (ECUs) and sensors which perform critical functions such as engine control. This paper presents a novel methodology for in-vehicle security through fingerprinting of ECUs. The proposed research uses the fingerprints injected in the signal due to material imperfections and semiconductor impurities. By extracting features from the physical CAN signal and using them as inputs for a machine learning algorithm, it is possible to determine the sender ECU of a packet. A high classification accuracy of up to 100.0% is possible when every node on the bus has a sufficiently different channel length. 

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