With the increasing connectivity enabled by the Internet of Things (IoT), security becomes a critical concern, and users should invest to secure their IoT applications. Due to the massive devices in the IoT network, users cannot be aware of the security policies taken by all its connected neighbors. Instead, a user makes security decisions based on the cyber risks that he perceives by observing a selected number of nodes. To this end, we propose a model which incorporates the limited attention or bounded rationality nature of players in the IoT. Specifically, each individual builds a sparse cognitive network of nodes to respond to. Based on this simplified cognitive network representation, each user then determines his security management policy by minimizing his own real-world security cost. The bounded rational decision-makings of players and their cognitive network formations are interdependent and thus should be addressed in a holistic manner. We establish a games-in-games framework and propose a Gestalt Nash equilibrium (GNE) solution concept to characterize the decisions of agents and quantify their risk of bounded perception due to the limited attention. In addition, we design a proximal-based iterative algorithm to compute the GNE. With case studies of smart communities, the designed algorithm can successfully identify the critical users whose decisions need to be taken into account by the other users during the security management.
Security management has been investigated in various research fields including computer networks @cite_14 , communications @cite_21 , cloud computing @cite_23 and infrastructures @cite_22 . With the advances in ICTs, a growing number of works have focused on the emerging critical issue of IoT security @cite_39 @cite_3 . Due to the interconnectivity between different agents, the security of one agent is also dependent on its connected ones which gives rise to the notion of interdependent security'' @cite_24 . The authors in @cite_42 @cite_25 @cite_4 @cite_15 have further investigated the security interdependencies in multilayer cyber-physical systems. The authors in @cite_2 @cite_18 @cite_0 have developed optimal contracts to address the cyber-physical security issues in IoT. In @cite_31 @cite_12 @cite_11 , the authors proposed optimal schemes for designing secure and resilient multi-layer IoT networks through graph-theoretical approaches.
@cite_18 @cite_14 @cite_4 @cite_22 @cite_31 @cite_21 @cite_42 @cite_3 @cite_39 @cite_24 @cite_0 @cite_23 @cite_2 @cite_15 @cite_25 @cite_12 @cite_11
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The massive proliferation of information and communications technologies (hardware and software) into the heart of modern critical infrastructures has given birth to a unique technological ecosystem. Despite the many advantages brought about by modern information and communications technologies, the shift from isolated environments to "systems-of-systems" integrated with massive information and communications infrastructures (e.g., the Internet) exposes critical infrastructures to significant cyber threats. Therefore, it is imperative to develop approaches for identifying and ranking assets in complex, large-scale and heterogeneous critical infrastructures. To address these challenges, this paper proposes a novel methodology for assessing the impacts of cyber attacks on critical infrastructures. The methodology is inspired by research in system dynamics and sensitivity analysis. The proposed behavioral analysis methodology computes the covariances of the observed variables before and after the execution of a specific intervention involving the control variables. Metrics are proposed for quantifying the significance of control variables and measuring the impact propagation of cyber attacks.Experiments conducted on the IEEE 14-bus and IEEE 300-bus electric grid models, and on the well-known Tennessee Eastman chemical process demonstrate the efficiency, scalability and cross-sector applicability of the proposed methodology in several attack scenarios. The advantages of the methodology over graph-theoretic and electrical centrality metric approaches are demonstrated using several test cases. Finally, a novel, stealthy cyber-physical attack is demonstrated against a simulated power grid; this attack can be used to analyze the precision of anomaly detection systems. With the increasing connectivity enabled by the Internet of Things (IoT), security becomes a critical concern, and users should invest to secure their IoT applications. Due to the massive devices in the IoT network, users cannot be aware of the security policies taken by all its connected neighbors. Instead, a user makes security decisions based on the cyber risks that he perceives by observing a selected number of nodes. To this end, we propose a model which incorporates the limited attention or bounded rationality nature of players in the IoT. Specifically, each individual builds a sparse cognitive network of nodes to respond to. Based on this simplified cognitive network representation, each user then determines his security management policy by minimizing his own real-world security cost. The bounded rational decision-makings of players and their cognitive network formations are interdependent and thus should be addressed in a holistic manner. We establish a games-in-games framework and propose a Gestalt Nash equilibrium (GNE) solution concept to characterize the decisions of agents and quantify their risk of bounded perception due to the limited attention. In addition, we design a proximal-based iterative algorithm to compute the GNE. With case studies of smart communities, the designed algorithm can successfully identify the critical users whose decisions need to be taken into account by the other users during the security management. Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving networks spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with prominent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processingstrategy. Weintroducea“Lazy Search"algorithmwhere the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named “Relative Selectivity" that is used to select between different query processing strategies. Our experiments performed on real online news, network traffic stream and a synthetic social network benchmark demonstrate 10-100x speedups over selectivity agnostic approaches. In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. However, it is also critical that security be at a reasonable resource and energy cost. In this article, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security resources in the network area. In particular, given a set of security resources R and a set of attacks to be faced A, our method chooses the subset of R that best addresses the attacks in A, and the set of locations where to place them, that ensure the security coverage of all IoT devices at minimum cost and energy consumption. We model our problem according to game theory and provide a Pareto-optimal solution in which the cost of the security infrastructure, its energy consumption, and the probability of a successful attack are minimized. Our experimental evaluation shows that our technique improves the system robustness in terms of packet delivery rate for different network topologies. Furthermore, we also provide a method for handling the computation of the resource allocation plan for large-scale networks scenarios, where the optimization problem may require an unreasonable amount of time to be solved. We show how our proposed method drastically reduces the computing time, while providing a reasonable approximation of the optimal solution. The cloud-enabled Internet of controlled things (IoCT) envisions a network of sensors, controllers, and actuators connected through a local cloud in order to intelligently control physical devices. Because cloud services are vulnerable to advanced persistent threats (APTs), each device in the IoCT must strategically decide whether to trust cloud services that may be compromised. In this paper, we present iSTRICT, an interdependent strategic trust mechanism for the cloud-enabled IoCT. iSTRICT is composed of three interdependent layers. In the cloud layer, iSTRICT uses FlipIt games to conceptualize APTs. In the communication layer, it captures the interaction between devices and the cloud using signaling games. In the physical layer, iSTRICT uses optimal control to quantify the utilities in the higher level games. Best response dynamics link the three layers in an overall “game-of-games,” for which the outcome is captured by a concept called Gestalt Nash equilibrium (GNE). We prove the existence of a GNE under a set of natural assumptions and develop an adaptive algorithm to iteratively compute the equilibrium. Finally, we apply iSTRICT to trust management for autonomous vehicles that rely on measurements from remote sources. We show that strategic trust in the communication layer achieves a worst-case probability of compromise for any attack and defense costs in the cyber layer. With the increasing connectivity enabled by the Internet of Things (IoT), security becomes a critical concern, and the users should invest to secure their IoT applications. Due to the massive devices in the IoT network, users cannot be aware of the security policies taken by all its connected neighbors. Instead, a user makes security decisions based on the cyber risks he perceives by observing a selected number of nodes. To this end, we propose a model which incorporates the limited attention or bounded rationality nature of players in the IoT. Specifically, each individual builds a sparse cognitive network which includes the users to respond to. Based on this simplified cognitive network representation, each user then determines his security investment policy by minimizing his own real-world security cost. The bounded rational decision-makings of players and their cognitive network formations are interdependent, and thus should be addressed in a holistic manner. We propose a Gestalt Nash equilibrium (GNE) solution concept to characterize the decisions of agents. Then, we design a proximal-based iterative algorithm to compute the GNE and show its convergence. With case studies to smart home communities, the designed algorithm can successfully identify the critical users whose decisions need to be taken into account by the other users during the security investment. In this paper, we consider a robust network control problem. We consider linear unstable and uncertain discrete time plants with a network between the sensor and controller and the controller and plant. We investigate the effect of data drop out in the form of packet losses. Four distinct control schemes are explored and sufficient conditions to ensure almost sure stability of the closed loop system are derived for each of them in terms of minimum packet arrival rate and the maximum uncertainty. I. INTRODUCTION In the past decade, networked control systems (NCS) have gained much attention from both the control com- munity and the network and communication community. When compared with classical feedback control system, networked control systems have several advantages. For example, they can reduce the system wiring, make the system easy to operate and maintain and later diagnose in case of malfunctioning, and increase system agility (20). Although NCS have advantages, inserting a network in between the plant and the controller introduces many problems as well. For instance, zero-delayed sensing and actuation, perfect information and synchronization are no longer guaranteed in the new system architecture as only finite bandwidth is available and data packet drops and delays may occur due to network traffic conditions. These must be revisited and analyzed before networked control systems become prevalent. Recently, many researchers have spent effort on these issues and some significant results were obtained and many are in progress. Many of the aforementioned issues are studied separately. Tatikonda (19) and Sahai (13) have presented some interesting results in the area of control under communication constraints. Specifically, Tatikonda gave a necessary and sufficient condition on the channel data rate such that a noiseless LTI system in the closed loop is asymptotically stable. He also gave rate results for stabilizing a noisy LTI system over a digital channel. Sahai proposed the notion of anytime capacity to deal with real time estimation and control for a networked control system. In our paper (17), the authors have considered various rate issues under finite bandwidth, packet drops and finite controls. An optimal bit allocation scheme is given in (16) under the networked setting. The effect of pacekt drops on state estimation was studied by Sinopoli, et. al. in (3). It has further been investigated by many researchers including the present authors in (15) and (6). We consider a cyber-physical system consisting of two interacting networks, i.e., a cyber network overlaying a physical network. It is envisioned that these systems are more vulnerable to attacks since node failures in one network may result in (due to the interdependence) failures in the other network, causing a cascade of failures that would potentially lead to the collapse of the entire infrastructure. The robustness of interdependent systems against this sort of catastrophic failure hinges heavily on the allocation of the (interconnecting) links that connect nodes in one network to nodes in the other network. In this paper, we characterize the optimum inter-link allocation strategy against random attacks in the case where the topology of each individual network is unknown. In particular, we analyze the “regular” allocation strategy that allots exactly the same number of bidirectional internetwork links to all nodes in the system. We show, both analytically and experimentally, that this strategy yields better performance (from a network resilience perspective) compared to all possible strategies, including strategies using random allocation, unidirectional interlinks, etc. The development of advanced wireless communication technologies and smart embedded control devices makes everything connected, leading to an emerging paradigm of the Internet of Controlled Things IoCT. IoCT consists of two layers of systems: cyber layer and physical layer. This work aims to establish a holistic framework that integrates the cyber-physical layers of the IoCT through the lens of contract theory. For the cyber layer, we use a FlipIt game to capture the cloud security. We focus on two types of cloud, high-type and low-type, in terms of their provided quality of service QoS. The cloud's type is of private information which is unknown to the contract maker. Therefore, the control system administrator CSA at the physical layer needs to design a menu of two contracts for each type of service provider SP due to this asymmetric information structure. According to the received contract, SP decides his cyber defense strategy in the FlipIt game of which the Nash equilibrium determines the QoS of the cloud, and further influences the physical system performance. The objective of CSA is to minimize the payment to the cloud SP and the control cost jointly by designing optimal contracts. Due to the interdependence between the cyber and physical layers in the cloud-enabled IoCT, we need to address the cloud security and contract design problems in an integrative manner. We find that CSA always requires the best QoS from two types of cloud. In addition, under the optimal contracts, the utilities of both SPs are constants. Furthermore, no contracts will be offered to the cloud if the resulting service cannot stabilize the physical system. The integration of modern information and communication technologies (ICTs) into critical infrastructures (CIs) improves its connectivity and functionalities yet also brings cyber threats. It is thus essential to understand the risk of ICTs on CIs holistically as a cyber-physical system and design efficient security hardening mechanisms. To this end, we capture the system behaviors of the CIs under malicious attacks and the protection strategies by a zero-sum game. We further propose a computationally tractable approximation for large-scale networks which builds on the factored graph that exploits the dependency structure of the nodes of CIs and the approximate dynamic programming tools for stochastic Markov games. This work focuses on a localized information structure and the single-controller game solvable by linear programming. Numerical results illustrate the proper tradeoff of the approximation accuracy and computation complexity in the new design paradigm and show the proactive security at the time of unanticipated attacks. In this paper, we design a secure, robust and resilient cyber-physical wind energy system (WES) from a cross-layer perspective. We establish a system-of-systems framework for the large-scale wind farm which includes the cyber and physical components. For the cyber layer, we use a game-theoretic model to capture the strategic behaviors of the network defender and the cyber attacker, and compute its mixed strategy Nash equilibria. For the physical layer, we design a robust and resilient switching controller using a Markov jump linear system model. Due to the interdependence between the cyber and physical systems, their performances are coupled and need to be designed in a holistic manner. To address this challenge, we propose an iterative algorithm to study the system-of-systems performance under the equilibrium design which jointly takes the cyber and physical layers into account. Case studies are provided to illustrate the interdependent design principles of cyber-physical WES. In this paper, we consider a delegated dynamic systemic cyber risk management problem between a resource owner (principal) and a risk manager (agent). The principal can only observe cyber risk outcomes of the network rather than the efforts that the agent spends on protecting the resources. Under this asymmetric information, the principal aims to minimize the systemic cyber risks by designing a dynamic contract that specifies the compensation flows and the anticipated efforts of the manager by taking into account his incentives and rational behaviors. We formulate a bi-level mechanism design problem for dynamic contract design which can be seen as a special class of differential game. We show that the principal has rational controllability of the systemic risk by designing an incentive compatible estimator of the agent's hidden efforts. We characterize the optimal mechanism design by reformulating the problem into a stochastic optimal control program and derive the solution explicitly. We further reveal a separation principle for dynamic risk management where the effort estimation and the compensation design can be achieved separately. Our modern era is characterized by a large-scale web of interconnected and interdependent economic and infrastructure systems, coupled with threats of terrorism. This paper demonstrates the value of introducing interdependency analysis into various phases of risk assessment and management through application of the Inoperability Input–Output Model (IIM). The IIM estimates the cascading inoperability and economic losses that result from interdependencies within large-scale economic and infrastructure systems. Based on real data and the Nobel Prize-winning W. Leontief economic model, the IIM is a computationally efficient, inexpensive, holistic method for estimating economic impacts. Three illustrative case studies are presented. The first and second illustrate how the supply- and demand-side IIM is used to calculate higher-order effects from attacks to vulnerabilities and implementation of risk management policies in large-scale economic systems. The final case study illustrates a more general use for interdependency analysis: to evaluate risk management options against multiple objectives. This study calculates a Pareto-optimal or efficient frontier of solutions by integrating a simplified model of the costs of recovery to the Power sector derived from open-source data with the IIM. Through these case studies, which use a database from the Bureau of Economic Analysis, we illustrate the value of interdependency analysis in the risk assessment and management process as an integral part of systems engineering. © 2005 Wiley Periodicals, Inc. Syst Eng 8: 323–341, 2005 This paper presents the application of cybersecurity to the operation and control of distributed electric power systems. In particular, the paper emphasizes the role of cybersecurity in the operation of microgrids and analyzes the dependencies of microgrid control and operation on information and communication technologies for cybersecurity. The paper discusses common cyber vulnerabilities in distributed electric power systems and presents the implications of cyber incidents on physical processes in microgrids. The paper examines the impacts of potential risks attributed to cyberattacks on microgrids and presents the affordable technologies for mitigating such risks. In addition, the paper presents a minimax-regret approach for minimizing the impending risks in managing microgrids. The paper also presents the opportunities provided by software-defined networking technologies to enhance the security of microgrid operations. It is concluded that cybersecurity could play a significant role in managing microgrid operations as microgrids strive for a higher degree of resilience as they supply power services to customers. With the remarkable growth of the Internet and communication technologies over the past few decades, Internet of Things (IoTs) is enabling the ubiquitous connectivity of heterogeneous physical devices with software, sensors, and actuators. IoT networks are naturally two-layer with the cloud and cellular networks coexisting with the underlaid device-todevice (D2D) communications. The connectivity of IoTs plays an important role in information dissemination for missioncritical and civilian applications. However, IoT communication networks are vulnerable to cyber attacks including the denialof- service (DoS) and jamming attacks, resulting in link removals in IoT network. In this work, we develop a heterogeneous IoT network design framework in which a network designer can add links to provide additional communication paths between two nodes or secure links against attacks by investing resources. By anticipating the strategic cyber attacks, we characterize the optimal design of secure IoT network by first providing a lower bound on the number of links a secure network requires for a given budget of protected links, and then developing a method to construct networks that satisfy the heterogeneous network design specifications. Therefore, each layer of the designed heterogeneous IoT network is resistant to a predefined level of malicious attacks with minimum resources. Finally, we provide case studies on the Internet of Battlefield Things (IoBT) to corroborate and illustrate our obtained results Industrial systems always prefer to reduce their operational expenses. To support such reductions, they need solutions that are capable of providing stability, fault tolerance, and flexibility. One such solution for industrial systems is cyber physical system (CPS) integration with the Internet of Things (IoT) utilizing cloud computing services. These CPSs can be considered as smart industrial systems, with their most prevalent applications in smart transportation, smart grids, smart medical and eHealthcare systems, and many more. These industrial CPSs mostly utilize supervisory control and data acquisition (SCADA) systems to control and monitor their critical infrastructure (CI). For example, WebSCADA is an application used for smart medical technologies, making improved patient monitoring and more timely decisions possible. The focus of the study presented in this paper is to highlight the security challenges that the industrial SCADA systems face in an IoT-cloud environment. Classical SCADA systems are already lacking in proper security measures; however, with the integration of complex new architectures for the future Internet based on the concepts of IoT, cloud computing, mobile wireless sensor networks, and so on, there are large issues at stakes in the security and deployment of these classical systems. Therefore, the integration of these future Internet concepts needs more research effort. This paper, along with highlighting the security challenges of these CI’s, also provides the existing best practices and recommendations for improving and maintaining security. Finally, this paper briefly describes future research directions to secure these critical CPSs and help the research community in identifying the research gaps in this regard. Despite much research on probabilistic key predistribution schemes for wireless sensor networks over the past decade, few formal analyses exist that define schemes’ resilience to node-capture attacks precisely and under realistic conditions. In this paper, we analyze the resilience of the @math -composite key predistribution scheme, which mitigates the node capture vulnerability of the Eschenauer-Gligor scheme in the neighbor discovery phase. We derive scheme parameters to have a desired level of resiliency, and obtain optimal parameters that defend against different adversaries as much as possible. We also show that this scheme can be easily enhanced to achieve the same “perfect resilience” property as in the random pairwise key predistribution for attacks launched after neighbor discovery. Despite considerable attention to this scheme, much prior work explicitly or implicitly uses an incorrect computation for the probability of link compromise under node-capture attacks and ignores the real-world transmission constraints of sensor nodes. Moreover, we derive the critical network parameters to ensure connectivity in both the absence and presence of node-capture attacks. We also investigate node replication attacks by analyzing the adversary’s optimal strategy.