Multi-agent network intrusion tolerance capability assessment method based on simulated annealing algorithm

Abstract

The invention discloses a multi-agent network intrusion tolerance capability assessment method based on simulated annealing algorithm. The simulated annealing technology is applied to the field of network intrusion tolerance capability assessment, and meanwhile, three alternative states are introduced when a network robustness calculation subset pair is updated on the basis of a traditional simulated annealing method, so that the subset pair has higher sampling randomness. The method has the advantage that the problem that large-scale multi-agent network intrusion tolerance capability assessment cannot be solved due to NP difficulty is solved.

Description

technical field [0001] The invention relates to the technical field of network intrusion tolerance assessment, in particular to a multi-agent network intrusion tolerance assessment method based on a simulated annealing algorithm. Background technique [0002] A multi-agent system refers to a large-scale network system composed of multiple autonomous agents with perception, communication, computing, and execution capabilities, and is widely used as a carrier for the realization of distributed collaborative algorithms. A multi-agent system not only has the advantages of resource sharing, good coordination, and strong autonomy that a general distributed system has, but also has strong robustness because each agent can solve large-scale complex problems through coordination and cooperation. Stickiness and reliability. However, in recent years, as network security risks have become increasingly prominent, network designers have paid more and more attention to the evaluation of n...

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Problems solved by technology

However, it has been proved in the literature that the evaluation and determination of the value pair (r, s) in robustness is an NP-hard problem

Therefore, the above traditional methods are only suitable for small multi-agent networks with a small number of nodes, and cannot be applied to large-scale networks with a large number of nodes.

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