12 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
Bayesian Nash Equilibrium Seeking for Distributed Incomplete-information Aggregative Games
In this paper, we consider a distributed Bayesian Nash equilibrium (BNE)
seeking problem in incomplete-information aggregative games, which is a
generalization of Bayesian games and deterministic aggregative games. We handle
the aggregation function for distributed incomplete-information situations.
Since the feasible strategies are infinite-dimensional functions and lie in a
non-compact set, the continuity of types brings barriers to seeking equilibria.
To this end, we discretize the continuous types and then prove that the
equilibrium of the derived discretized model is an -BNE. On this
basis, we propose a distributed algorithm for an -BNE and further
prove its convergence
Bayesian Nash equilibrium seeking for multi-agent incomplete-information aggregative games
summary:In this paper, we consider a distributed Bayesian Nash equilibrium (BNE) seeking problem in incomplete-information aggregative games, which is a generalization of either Bayesian games or deterministic aggregative games. We handle the aggregation function to adapt to incomplete-information situations. Since the feasible strategies are infinite-dimensional functions and lie in a non-compact set, the continuity of types brings barriers to seeking equilibria. To this end, we discretize the continuous types and then prove that the equilibrium of the derived discretized model is an -BNE. On this basis, we propose a distributed algorithm for an -BNE and further prove its convergence
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Automated machine learning (AutoML) seeks to build ML models with minimal
human effort. While considerable research has been conducted in the area of
AutoML in general, aiming to take humans out of the loop when building
artificial intelligence (AI) applications, scant literature has focused on how
AutoML works well in open-environment scenarios such as the process of training
and updating large models, industrial supply chains or the industrial
metaverse, where people often face open-loop problems during the search
process: they must continuously collect data, update data and models, satisfy
the requirements of the development and deployment environment, support massive
devices, modify evaluation metrics, etc. Addressing the open-environment issue
with pure data-driven approaches requires considerable data, computing
resources, and effort from dedicated data engineers, making current AutoML
systems and platforms inefficient and computationally intractable.
Human-computer interaction is a practical and feasible way to tackle the
problem of open-environment AI. In this paper, we introduce OmniForce, a
human-centered AutoML (HAML) system that yields both human-assisted ML and
ML-assisted human techniques, to put an AutoML system into practice and build
adaptive AI in open-environment scenarios. Specifically, we present OmniForce
in terms of ML version management; pipeline-driven development and deployment
collaborations; a flexible search strategy framework; and widely provisioned
and crowdsourced application algorithms, including large models. Furthermore,
the (large) models constructed by OmniForce can be automatically turned into
remote services in a few minutes; this process is dubbed model as a service
(MaaS). Experimental results obtained in multiple search spaces and real-world
use cases demonstrate the efficacy and efficiency of OmniForce
Zero-determinant strategy in stochastic Stackelberg asymmetric security game
Abstract In a stochastic Stackelberg asymmetric security game, the strong Stackelberg equilibrium (SSE) strategy is a popular option for the defender to get the highest utility against an attacker with the best response (BR) strategy. However, the attacker may be a boundedly rational player, who adopts a combination of the BR strategy and a fixed stubborn one. In such a condition, the SSE strategy may not maintain the defensive performance due to the stubbornness. In this paper, we focus on how the defender can adopt the unilateral-control zero-determinate (ZD) strategy to confront the boundedly rational attacker. At first, we verify the existence of ZD strategies for the defender. We then investigate the performance of the defenderâs ZD strategy against a boundedly rational attacker, with a comparison of the SSE strategy. Specifically, when the attackerâs strategy is close to the BR strategy, the ZD strategy admits a bounded loss for the defender compared with the SSE strategy. Conversely, when the attackerâs strategy is close to the stubborn strategy, the ZD strategy can bring higher defensive performance for the defender than the SSE strategy does
Quantitative Assessment of Domino Effect Caused by Heat Radiation in Industrial Sites
International audienceAccidents caused by the domino effect or chain of accidents are the most destructive accidents related to industrial sites. The probability of domino effects is increasingly high due to the development of industrial complexes, their proximity, and storage of dangerous substances, transportation networks and population growth. Fires are among the most frequent accidents due to the installations or storage equipment under pressure, and storage of flammable substances. The thermal radiation generated by fire is one of the main factors leading to domino effects and may cause severe consequences on industrial sites, people, structures, environment and economy. This paper presents a methodology for quantitative assessment of domino effect caused by heat radiation on storage areas, a model for the estimation of the human vulnerability is also proposed, individual and societal risk are estimated. The results have proved the importance of domino effect in quantitative risk analysis