138 research outputs found
Defense for Advanced Persistent Threat with Inadvertent or Malicious Insider Threats
In this paper, we propose a game-theoretical framework to investigate
advanced persistent threat problems with two types of insider threats:
malicious and inadvertent. Within this framework, a unified three-player game
is established and Nash equilibria are obtained in response to different
insiders. By analyzing Nash equilibria, we provide quantitative solutions to
the advanced persistent threat problems with insider threats. Furthermore,
optimal defense strategy and defender's cost comparisons between two insider
threats have been performed. The findings suggest that the defender should
employ more active defense strategies against inadvertent insider threats than
against malicious insider threats, despite the fact that malicious insider
threats cost the defender more. Our theoretical analysis is validated by
numerical results, including an additional examination of the conditions of the
risky strategies adopted by different insiders. This may help the defender in
determining monitoring intensities and defensive strategies
Distributed Algorithm for Continuous-type Bayesian Nash Equilibrium in Subnetwork Zero-sum Games
In this paper, we consider a continuous-type Bayesian Nash equilibrium (BNE)
seeking problem in subnetwork zero-sum games, which is a generalization of
deterministic subnetwork zero-sum games and discrete-type Bayesian zero-sum
games. In this continuous-type model, because the feasible strategy set is
composed of infinite-dimensional functions and is not compact, it is hard to
seek a BNE in a non-compact set and convey such complex strategies in network
communication. To this end, we design two steps to overcome the above
bottleneck. One is a discretization step, where we discretize continuous types
and prove that the BNE of the discretized model is an approximate BNE of the
continuous model with an explicit error bound. The other one is a communication
step, where we adopt a novel compression scheme with a designed sparsification
rule and prove that agents can obtain unbiased estimations through compressed
communication. Based on the above two steps, we propose a distributed
communication-efficient algorithm to practicably seek an approximate BNE, and
further provide an explicit error bound and an convergence
rate.Comment: Submitted to SIAM Journal on Control and Optimizatio
Transient Performance Simulation of Aircraft Engine Integrated with Fuel and Control Systems
A new method for the simulation of gas turbine fuel systems based on an inter-component volume method has been developed. It is able to simulate the performance of each of the hydraulic components of a fuel system using physics-based models, which potentially offers more accurate results compared with those using transfer functions. A transient performance simulation system has been set up for gas turbine engines based on an inter-component volume (ICV) method. A proportional-integral (PI) control strategy is used for the simulation of engine controller. An integrated engine and its control and hydraulic fuel systems has been set up to investigate their coupling effect during engine transient processes. The developed simulation system has been applied to a model aero engine. The results show that the delay of the engine transient response due to the inclusion of the fuel system model is noticeable although relatively small. The developed method is generic and can be applied to any other gas turbines and their control and fuel systems
Online Game with Time-Varying Coupled Inequality Constraints
In this paper, online game is studied, where at each time, a group of players
aim at selfishly minimizing their own time-varying cost function simultaneously
subject to time-varying coupled constraints and local feasible set constraints.
Only local cost functions and local constraints are available to individual
players, who can share limited information with their neighbors through a fixed
and connected graph. In addition, players have no prior knowledge of future
cost functions and future local constraint functions. In this setting, a novel
decentralized online learning algorithm is devised based on mirror descent and
a primal-dual strategy. The proposed algorithm can achieve sublinearly bounded
regrets and constraint violation by appropriately choosing decaying stepsizes.
Furthermore, it is shown that the generated sequence of play by the designed
algorithm can converge to the variational GNE of a strongly monotone game, to
which the online game converges. Additionally, a payoff-based case, i.e., in a
bandit feedback setting, is also considered and a new payoff-based learning
policy is devised to generate sublinear regrets and constraint violation.
Finally, the obtained theoretical results are corroborated by numerical
simulations.Comment: arXiv admin note: text overlap with arXiv:2105.0620
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