Network security traceability semantic recognition method based on prompt self-supervised learning

Abstract

The invention discloses a network security traceability semantic recognition method based on prompt self-supervised learning. The method comprises the following steps: constructing a network security professional corpus; multidimensional enrichment is realized, and a data set for capturing an attack source in a dialogue is reconstructed; the transformer coding part identifies semantic features and carries out vectorization representation; decoding and selecting key semantics by using a transformer; training cross entropy loss with a real label, and training model parameters; iterating the optimization model for multiple times to output a corresponding label, and identifying a corresponding IP or domain name; according to the method, a transformer model is used as a basic structure of a mask language model, semantic recognition can be carried out on texts according to external information and context content, prompt learning of the mask language model can be carried out for network security specialized vocabularies with non-rich data set resources to fully mine information of existing data, and therefore high efficiency and high efficiency are achieved. And low-cost semantic extraction enables a machine to understand the intention of a real person.

Description

technical field [0001] The invention relates to the field of network security natural language processing, in particular to a method for identifying network security traceability semantics based on prompt self-supervised learning. Background technique [0002] With the development of the era of information and data, people's awareness of network security has gradually increased. The security confrontation driven by data and intelligence, and the level of automation and intelligence of technology platforms have increasingly become the focus of the struggle between offense and defense in cyberspace. Semantic recognition of network security traceability is one of the main tasks of intelligent security operations. The key technology of semantic recognition of network security traceability is to extract the core information blocks from the document consisting of chat records in the war room into summarized summaries. Semantic identification of security event traceability can be b...

AI Technical Summary

Problems solved by technology

[0005] (1) The semantic recognition task of network security traceability under supervised learning requires a large amount of data to capture attack sources for information traceability, but the data set resources in the professional field of network security are not abundant, and the cost of manually annotated supervised data is too high;

[0006] (2) The same attention should be paid to the feature extraction of text sequences input by different roles in the war room, such as combat staff, general commander, and disposal personnel. In other words, the cyclic neural network model architecture remembers all the information in the war room text , which causes the key information in a sentence to depend on the physical position of the word in the sentence, not on the meaning of the word itself, which will lead to redundant memory information;

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