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1. Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

   https://doi.org/10.1109/COMST.2020.3036778  

   Felix Olowononi, Danda B. (September 2020, IEEE Communications surveys and tutorials)

   null (Ed.)

   Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This article is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this article, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area. 

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2. 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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3. Cyber-Physical System Development Environment for Energy Applications

   https://doi.org/10.1115/ES2017-3589  

   Roth, Thomas; Song, Eugene; Burns, Martin; Neema, Himanshu; Emfinger, William; Sztipanovits, Janos (June 2017, ASME 2017 11th International Conference on Energy Sustainability)

   Cyber-physical systems (CPS) are smart systems that include engineered interacting networks of physical and computational components. The tight integration of a wide range of heterogeneous components enables new functionality and quality of life improvements in critical infrastructures such as smart cities, intelligent buildings, and smart energy systems. One approach to study CPS uses both simulations and hardware-in-the-loop (HIL) to test the physical dynamics of hardware in a controlled environment. However, because CPS experiment design may involve domain experts from multiple disciplines who use different simulation tool suites, it can be a challenge to integrate the heterogeneous simulation languages and hardware interfaces into a single experiment. The National Institute of Standards and Technology (NIST) is working on the development of a universal CPS environment for federation (UCEF) that can be used to design and run experiments that incorporate heterogeneous physical and computational resources over a wide geographic area. This development environment uses the High Level Architecture (HLA), which the Department of Defense has advocated for co-simulation in the field of distributed simulations, to enable communication between hardware and different simulation languages such as Simulink® and LabVIEW®. This paper provides an overview of UCEF and motivates how the environment could be used to develop energy experiments using an illustrative example of an emulated heat pump system. 

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4. CySec Game: A Framework and Tool for Cyber Risk Assessment and Security Investment Optimization in Critical Infrastructures

   https://doi.org/10.1109/RWS55399.2022.9984040  

   Hyder, Burhan; Majerus, Harrison; Sellars, Hayden; Greazel, Jonathan; Strobel, Joseph; Battani, Nicholas; Peng, Stefan; Govindarasu, Manimaran (September 2022, Resilience Week Symposium (RWS))

   Cyber physical system (CPS) Critical infrastructures (CIs) like the power and energy systems are increasingly becoming vulnerable to cyber attacks. Mitigating cyber risks in CIs is one of the key objectives of the design and maintenance of these systems. These CPS CIs commonly use legacy devices for remote monitoring and control where complete upgrades are uneconomical and infeasible. Therefore, risk assessment plays an important role in systematically enumerating and selectively securing vulnerable or high-risk assets through optimal investments in the cybersecurity of the CPS CIs. In this paper, we propose a CPS CI security framework and software tool, CySec Game, to be used by the CI industry and academic researchers to assess cyber risks and to optimally allocate cybersecurity investments to mitigate the risks. This framework uses attack tree, attack-defense tree, and game theory algorithms to identify high-risk targets and suggest optimal investments to mitigate the identified risks. We evaluate the efficacy of the framework using the tool by implementing a smart grid case study that shows accurate analysis and feasible implementation of the framework and the tool in this CPS CI environment. 

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5. Automated Adversary-in-the-Loop Cyber-Physical Defense Planning

   https://doi.org/10.1145/3596222  

   Banik, Sandeep; Ramachandran, Thiagarajan; Bhattacharya, Arnab; Bopardikar, Shaunak D. (July 2023, ACM Transactions on Cyber-Physical Systems)

   Security of cyber-physical systems (CPS) continues to pose new challenges due to the tight integration and operational complexity of the cyber and physical components. To address these challenges, this article presents a domain-aware, optimization-based approach to determine an effective defense strategy for CPS in an automated fashion—by emulating a strategic adversary in the loop that exploits system vulnerabilities, interconnection of the CPS, and the dynamics of the physical components. Our approach builds on an adversarial decision-making model based on a Markov Decision Process (MDP) that determines the optimal cyber (discrete) and physical (continuous) attack actions over a CPS attack graph. The defense planning problem is modeled as a non-zero-sum game between the adversary and defender. We use a model-free reinforcement learning method to solve the adversary’s problem as a function of the defense strategy. We then employ Bayesian optimization (BO) to find an approximatebest-responsefor the defender to harden the network against the resulting adversary policy. This process is iterated multiple times to improve the strategy for both players. We demonstrate the effectiveness of our approach on a ransomware-inspired graph with a smart building system as the physical process. Numerical studies show that our method converges to a Nash equilibrium for various defender-specific costs of network hardening. 

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