Method and system for visualizing attack and defense strategies

Abstract

The present invention provides a method and system for visualizing attack and defense strategies, which solves the problems that existing attack types are difficult to update and define, and defense strategies cannot be effectively deployed. Classified feature vectors are obtained to obtain multi-dimensional sets, and machine learning models are used to detect attacks, and a class of detected attacks is identified as the type of attack, targeted source tracing is carried out, attack coverage is morphologically analyzed, and historical attack data is matched for prediction. The historical attack data predicts the strategic options and probabilities of the offensive and defensive sides, and administrators can deploy defensive strategies on the interface.

Description

technical field [0001] The present application relates to the technical field of network security, in particular to a method and system for visualizing attack defense strategies. Background technique [0002] With the rapid development of network technology, it is becoming more and more difficult to define the type of attack, and it is difficult to use the corresponding effective defense strategy without knowing the type of attack. Therefore, knowing the type of attack is the first step in attack detection. This application creatively uses a method of defining broad types of attacks. [0003] In the past, the deployment of defense strategies was either man-made or system background deployment, which lacked the cooperation of man and machine, and lacked the support of historical data. Only when the deployment strategy failed, would the deployment be known to be insufficient, which is very unfavorable for enterprise use. [0004] Therefore, there is an urgent need for a metho...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a method and system for visualizing attack defense strategies to solve the problems that existing attack types are difficult to update and define, and defense strategies cannot be effectively deployed. The class identifier extracts the classified feature vector to obtain a multi-dimensional set, and sends the multi-dimensional set to the machine learning model for attack detection. The detected class of attack marks is used as the type of attack, and targeted source tracing is carried out to conduct morphological analysis on the attack coverage. Predict historical attack data, and administrators can deploy defense strategies based on options and probabilities on the interface

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