1,480 research outputs found

    A Game-Theoretic Foundation for the Maximum Software Resilience against Dense Errors

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    Safety-critical systems need to maintain their functionality in the presence of multiple errors caused by component failures or disastrous environment events. We propose a game-theoretic foundation for synthesizing control strategies that maximize the resilience of a software system in defense against a realistic error model. The new control objective of such a game is called kk -resilience. In order to be kk -resilient, a system needs to rapidly recover from infinitely many waves of a small number of up to kk close errors provided that the blocks of up to kk errors are separated by short time intervals, which can be used by the system to recover. We first argue why we believe this to be the right level of abstraction for safety critical systems when local faults are few and far between. We then show how the analysis of kk -resilience problems can be formulated as a model-checking problem of a mild extension to the alternating-time μ\mu -calculus (AMC). The witness for kk resilience, which can be provided by the model checker, can be used for providing control strategies that are optimal with respect to resilience. We show that the computational complexity of constructing such optimal control strategies is low and demonstrate the feasibility of our approach through an implementation and experimental results

    Game-Theoretic and Machine-Learning Techniques for Cyber-Physical Security and Resilience in Smart Grid

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    The smart grid is the next-generation electrical infrastructure utilizing Information and Communication Technologies (ICTs), whose architecture is evolving from a utility-centric structure to a distributed Cyber-Physical System (CPS) integrated with a large-scale of renewable energy resources. However, meeting reliability objectives in the smart grid becomes increasingly challenging owing to the high penetration of renewable resources and changing weather conditions. Moreover, the cyber-physical attack targeted at the smart grid has become a major threat because millions of electronic devices interconnected via communication networks expose unprecedented vulnerabilities, thereby increasing the potential attack surface. This dissertation is aimed at developing novel game-theoretic and machine-learning techniques for addressing the reliability and security issues residing at multiple layers of the smart grid, including power distribution system reliability forecasting, risk assessment of cyber-physical attacks targeted at the grid, and cyber attack detection in the Advanced Metering Infrastructure (AMI) and renewable resources. This dissertation first comprehensively investigates the combined effect of various weather parameters on the reliability performance of the smart grid, and proposes a multilayer perceptron (MLP)-based framework to forecast the daily number of power interruptions in the distribution system using time series of common weather data. Regarding evaluating the risk of cyber-physical attacks faced by the smart grid, a stochastic budget allocation game is proposed to analyze the strategic interactions between a malicious attacker and the grid defender. A reinforcement learning algorithm is developed to enable the two players to reach a game equilibrium, where the optimal budget allocation strategies of the two players, in terms of attacking/protecting the critical elements of the grid, can be obtained. In addition, the risk of the cyber-physical attack can be derived based on the successful attack probability to various grid elements. Furthermore, this dissertation develops a multimodal data-driven framework for the cyber attack detection in the power distribution system integrated with renewable resources. This approach introduces the spare feature learning into an ensemble classifier for improving the detection efficiency, and implements the spatiotemporal correlation analysis for differentiating the attacked renewable energy measurements from fault scenarios. Numerical results based on the IEEE 34-bus system show that the proposed framework achieves the most accurate detection of cyber attacks reported in the literature. To address the electricity theft in the AMI, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed, which is equipped with Benford’s analysis for initial diagnostics on large smart meter data. A Stackelberg game between utility and multiple electricity thieves is then formulated to model the electricity theft actions. Finally, a Likelihood Ratio Test (LRT) is utilized to detect potentially fraudulent meters

    Resilience of a deer hunting system in Southeast Alaska: integrating social, ecological, and genetic dimensions

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    Thesis (Ph.D.) University of Alaska Fairbanks, 2009I examined the interactions of key components of a hunting system of Sitka black-tailed deer (Odocoileus hemionus sitkensis) on Prince of Wales Island, Alaska to address concerns of subsistence hunters and to provide a new tool to more effectively monitor deer populations. To address hunter concerns, I documented local knowledge and perceptions of changes in harvest opportunities of deer over the last 50 years as a result of landscape change (e.g., logging, roads). To improve deer monitoring, I designed an efficient method to sample and survey deer pellets, tested the feasibility of identifying individual deer from fecal DNA, and used DNA-based mark and recapture techniques to estimate population trends of deer. I determined that intensive logging from 1950 into the 1990s provided better hunter access to deer and habitat that facilitated deer hunting. However, recent declines in logging activity and successional changes in logged forests have reduced access to deer and increased undesirable habitat for deer hunting. My findings suggested that using DNA from fecal pellets is an effective method for monitoring deer in southeast Alaska. My sampling protocol optimized encounter rates with pellet groups allowing feasible and efficient estimates of deer abundance. I estimated deer abundance with precision (±20%) each year in 3 distinct watersheds, and identified a 30% decline in the deer population between 2006-2008. My data suggested that 3 consecutive severe winters caused the decline. Further, I determined that managed forest harvested>30 years ago supported fewer deer relative to young-managed forest and unmanaged forest. I provided empirical data to support both the theory that changes in plant composition because of succession of logged forest may reduce habitat carrying capacity of deer over the long-term (i.e., decades), and that severity of winter weather may be the most significant force behind annual changes in deer population size in southeast Alaska. Adaptation at an individual and institutional level may be needed to build resilience into the hunting system as most (>90%) of logged forest in southeast Alaska transitions over the next couple of decades into a successional stage that sustains fewer deer and deer hunting opportunities.1. General introduction -- 2. Influence of hunter adaptability on resilience of subsistence hunting systems -- 3. Linking hunter knowledge with forest change to understand changing deer harvest opportunities in intensively logged landscapes -- 4. Individual identification of Sitka black-tailed deer using DNA from fecal pellets -- 5. A practical approach for sampling along animal trails -- 6. Estimating abundance of Sitka black-tailed deer using DNA from fecal pellets -- 7. Summary -- 8. Future recommendations -- Appendix

    Strategic Investment in Protection in Networked Systems

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    We study the incentives that agents have to invest in costly protection against cascading failures in networked systems. Applications include vaccination, computer security and airport security. Agents are connected through a network and can fail either intrinsically or as a result of the failure of a subset of their neighbors. We characterize the equilibrium based on an agent's failure probability and derive conditions under which equilibrium strategies are monotone in degree (i.e. in how connected an agent is on the network). We show that different kinds of applications (e.g. vaccination, malware, airport/EU security) lead to very different equilibrium patterns of investments in protection, with important welfare and risk implications. Our equilibrium concept is flexible enough to allow for comparative statics in terms of network properties and we show that it is also robust to the introduction of global externalities (e.g. price feedback, congestion).Comment: 32 pages, 3 figure

    Cyber Threat Intelligence based Holistic Risk Quantification and Management

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