Storm-based Markov equivalence class model distributed learning method

Abstract

The invention discloses a Storm-based Markov equivalence class model distributed learning method. The method comprises the following steps that 1, a computing node of a cluster is created by using a Storm framework; 2, an initialization node creates an initial Markov equivalence class state and generates a state primitive set; 3, a search node calculates all legal modification operators of the current state primitive set and applies the operators to the current state primitive set; 4, a scoring node calculates a fitting degree score of a model for a dataset by using a minimum description length criterion; and 5, an output node judges whether the state primitive set reaches local optimum or not and finally obtains a Markov equivalence class model most matched with network flow data. According to the method, the advantage of distributed storage in calculation process acceleration is fully utilized and the real-time processing capability of an invasion detection system for the network flow data is improved.

Description

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AI Technical Summary

Benefits of technology

This patented technology helps solve problems related to distributing complex computational graphs efficiently without overlapping or interferring parts that may be difficult for other systems to handle due to their structure. It uses a special type called storm (a computer program used during training). By utilizing these techniques, researchers can create highly accurate simulations of networks while reducing complexity compared to previous methods. Additionally, storing and processing big datasets quickly becomes easier because they are organized into smaller spaces instead of spread out across multiple servers. Overall, this new approach provides better efficiency than existing solutions.

Problems solved by technology

This patents discusses different techniques related to studying complex networks like TCP/IP protocol stacks or routers. However, these approaches are complicated due to their dependence structure and require significant computational resources. Therefore there needs better ways to learn this type of model without overwhelming up any memory capacity required.

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