A user-defined dynamic collection method of massive network security data

Abstract

The invention discloses a user-defined massive-network-security-data dynamic-collection method. The method includes: acquiring data source information; establishing a set formed by existing collectionstrategies and user-defined collection strategies; binding data sources and applicable collection strategies to form collection tasks; creating a collection management node to manage and allocate thecollection tasks; executing the collection tasks to create and deploy multiple collection agents in a distributed manner; acquiring performance data of the collection agents and collection-task execution situation data; and carrying out dynamic allocation of the collection tasks according to performance data of the collection agents. According to the method, the user-defined collection strategiesare provided in establishing the collection tasks, new data sources are adapted in time, implementation is easy, redundant upgrading operations are reduced, tasks are dynamically allocated through the management node, efficiency of collection-agent task-execution is adjusted, and high efficiency and real-time performance of data collection in a massive-data environment are guaranteed.

Description

technical field [0001] The invention relates to the technical field of computer network applications, in particular to a user-defined method for dynamically collecting mass network security data. Background technique [0002] Every network security device on the Internet generates a large amount of security data in real time. In order to better play the role of network security equipment and monitor and prevent security incidents, it is necessary to analyze and process log security data. Through the processing of massive data, real and accurate security incidents can be efficiently mined to improve the ability of emergency response. In the current big data environment, data acquisition has become very important. In every acquisition system application, there will be problems of incomplete data sources, poor adaptability, and low efficiency. [0003] E.g: [0004] Scenario 1: Most collection systems have built-in collection strategies for common data sources. Usually, col...

AI Technical Summary

Problems solved by technology

[0011] The allocation method of collection tasks is fixed allocation, and the tasks cannot be allocated on demand, resulting in unbalanced load on the collection nodes

As a result, the efficiency of data collection cannot be optimal

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