Vulnerability utilization risk evaluation method based on multi-source word embedding and knowledge map

Abstract

The invention discloses a vulnerability utilization risk evaluation method based on multi-source word embedding and a knowledge map. The method comprises steps of building data source bodies associated with network security according to an information security knowledge map, then acquiring latest online information from different online data sources; preprocessing data and performing word vectorizatio by using word embedding; mapping data underwent word vectorization to a relation chart between entities and entities according to the knowledge map; and building a vulnerability utilization riskevaluation model by combining historical data sets and the knowledge map, evaluating vulnerability utilization risk, and delivering evaluation basis for vulnerability utilization risks. In the method,vulnerability utilization possibility is evaluated more comprehensively by combining network attack development logics, with the knowledge map as a reference, the method provides valuable reference for security defense, further, the method provides a reliability computing model, so that information really associated with vulnerability utilization prediction can be found in massive fragment information, and evaluation is more accurate.

Description

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AI Technical Summary

Benefits of technology

This patented system allows us to evaluate different types of threats through an ensemble of data sources with varying degrees of impact or damage (attack methods). It also considers factors like network security scenarios, risk levels associated with each type of threat, and other aspects such as correlations among them. By analyzing these features over time we aimed at developing models that accurately predict potential future exposure to potentially malicious activities.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving understanding about threats that may be used against computer networks or cybersecurity organizations through analysis from various sources like databases with different types of weaknesses. Current methods only rely heavily upon manual inspections and human judgment, making them difficult to identify specific areas where issues could occur due to hidden holes caused by external factors like weather conditions or physical damage.

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