Increasing connectivity of communication networks enables large-scale distributed processing over networks and improves the efficiency for information exchange. However, malware and virus can take advantage of the high connectivity to spread over the network and take control of devices and servers for illicit purposes. In this paper, we use an SIS epidemic model to capture the virus spreading process and develop a virus-resistant weight adaptation scheme to mitigate the spreading over the network. We propose a differential game framework to provide a theoretic underpinning for decentralized mitigation in which nodes of the network cannot fully coordinate, and each node determines its own control policy based on local interactions with neighboring nodes. We characterize and examine the structure of the Nash equilibrium, and discuss the inefficiency of the Nash equilibrium in terms of minimizing the total cost of the whole network. A mechanism design through a penalty scheme is proposed to reduce the inefficiency of the Nash equilibrium and allow the decentralized policy to achieve social welfare for the whole network. We corroborate our results using numerical experiments and show that virus-resistance can be achieved by a distributed weight adaptation scheme.

Abstract-Microelectromechanical systems (MEMS) represents a technology that integrates miniaturized mechanical and electromechanical components (i.e., sensors and actuators) that are made using microfabrication techniques. MEMS devices have become an essential component in a wide range of applications, ranging from medical and military to consumer electronics. As MEMS technology is implemented in a growing range of areas, the reliability of MEMS devices is a concern. Understanding the failure mechanisms is a prerequisite for quantifying and improving the reliability of MEMS devices. This paper reviews the common failure mechanisms in MEMS, including mechanical fracture, fatigue, creep, stiction, wear, electrical short and open, contamination, their effects on devices' performance, inspection techniques, and approaches to mitigate those failures through structure optimization and material selection.

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This paper studies reinforcement learning (RL) under malicious falsification on cost signals and introduces a quantitative framework of attack models to understand the vulnerabilities of RL. Focusing on Q-learning, we show that Q-learning algorithms converge under stealthy attacks and bounded falsifications on cost signals. We characterize the relation between the falsified cost and the Q-factors as well as the policy learned by the learning agent which provides fundamental limits for feasible offensive and defensive moves. We propose a robust region in terms of the cost within which the adversary can never achieve the targeted policy. We provide conditions on the falsified cost which can mislead the agent to learn an adversary's favored policy. A numerical case study of water reservoir control is provided to show the potential hazards of RL in learningbased control systems and corroborate the results.

We study epidemic spreading in a random walk network where agents with heterogeneous interaction radius randomly walk in a planar space. We obtain the explicit expression of epidemic threshold which indicates that the heterogeneity of interaction radius decreases the threshold. Concretely, the greater the variance of the radius distribution is, the smaller the epidemic threshold will be. Simulation results about the epidemic threshold match well with our theoretical results. In simulation study, the infection density in steady state, which is called the final density, is investigated. When there are two dierent values of radius, a larger mean value of radius increases the final density. However, although an increasing second order origin moment of radius makes the epidemic easier to outbreak, it also lowers the final density of infected individuals.

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Modern control systems are featured by their hierarchical structure composing of cyber, physical, and human layers. The intricate dependencies among multiple layers and units of modern control systems require an integrated framework to address cross-layer design issues related to security and resilience challenges. To this end, game theory provides a bottom-up modeling paradigm to capture the strategic interactions among multiple components of the complex system and enables a holistic view to understand and design cyber-physical-human control systems. In this review, we first provide a multi-layer perspective toward increasingly complex and integrated control systems and then introduce several variants of dynamic games for modeling different layers of control systems. We present game-theoretic methods for understanding the fundamental tradeoffs of robustness, security, and resilience and developing a cross-layer approach to enhance the system performance in various adversarial environments. This review also includes three quintessential research problems that represent three research directions where dynamic game approaches can bridge between multiple research areas and make significant contributions to the design of modern control systems. The paper is concluded with a discussion on emerging areas of research that crosscut dynamic games and control systems.