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.