Method and device for strategy self-learning and optimization based on random forest

A random forest algorithm and random forest technology are applied in the field of strategy self-learning and optimization methods and devices based on random forest, which can solve problems such as poor accuracy of security inspection results, so as to alleviate poor accuracy and reduce false negatives and false positives. The effect of reporting probabilities

Active Publication Date: 2021-02-26
HANGZHOU ANHENG INFORMATION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] In view of this, the object of the present invention is to provide a random forest-based policy self-learning and optimization method and device to alleviate the technical problem of poor accuracy of security inspection results of traditional security policies

Method used

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  • Method and device for strategy self-learning and optimization based on random forest
  • Method and device for strategy self-learning and optimization based on random forest
  • Method and device for strategy self-learning and optimization based on random forest

Examples

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Effect test

Embodiment 1

[0047] A random forest-based strategy self-learning and optimization method provided by the embodiment of the present invention, such as figure 1 shown, including:

[0048] Step S102, determining the correct rate of the target security policy, wherein the correct rate is used to indicate the accuracy of the target security policy when making a security judgment on the network application service;

[0049] Step S104, judging whether the accuracy rate is less than a preset tuning threshold;

[0050] In step S106, when the correct rate is less than the preset tuning threshold, the random forest algorithm is used to optimize the original security policy model of the target security policy to obtain the optimized security policy model, thereby completing the optimization of the target security policy.

[0051] Specifically, the target security policy has an original security policy model to determine the correct rate of the target security policy, that is, to determine the correct...

Embodiment 2

[0094] A random forest-based policy self-learning and optimization device provided by an embodiment of the present invention, such as Figure 4 shown, including:

[0095] The first determination module 100 is configured to determine the correct rate of the target security policy, wherein the correct rate is used to indicate the accuracy of the target security policy when making a security judgment on the network application service;

[0096] A judging module 200, configured to judge whether the accuracy rate is less than a preset tuning threshold;

[0097] The second determination module 300 is configured to use the random forest algorithm to optimize the original security policy model of the target security policy when the accuracy rate is less than the preset tuning threshold, to obtain the optimized security policy model, thereby completing the target security policy Optimization.

[0098] In the embodiment of the present invention, the first determination module 100 dete...

Embodiment 3

[0117] An embodiment of the present invention provides a computer-readable medium having a non-volatile program code executable by a processor, and the program code causes the processor to execute the method of Embodiment 1.

[0118] Specifically, the storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and other media that can store program codes. .

[0119] In this embodiment of the present invention, the program code causes the processor to execute the method of Embodiment 1, that is, firstly determine the accuracy rate of the target security policy, where the accuracy rate is used to indicate the security rate of the target security policy when making a security judgment on the network application service. degree of accuracy; then, it is judged whether the correct rate is less than the preset tuning threshold; finally, when the corr...

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Abstract

The present invention provides a strategy self-learning and optimization method and device based on random forest, which relates to the technical field of network security. The degree of accuracy of the security judgment of the application service; determine whether the correct rate is less than the preset tuning threshold; if the correct rate is less than the preset tuning threshold, use the random forest algorithm to optimize the original security policy model of the target security policy , to obtain the optimized security policy model, so as to complete the optimization of the target security policy. The invention alleviates the technical problem that the accuracy of the safety inspection result of the traditional safety policy is poor.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to a random forest-based strategy self-learning and optimization method and device. Background technique [0002] With the development of the network and the popularization of network application services, the security of network application services is directly related to the interests of the majority of network users. Commonly used network application services include: website construction, virtual hosting, enterprise mailboxes, and some OA and CRM software The security of network application services is an important part of network information security. [0003] At present, the security technology system of network information systems often guides the reasonable configuration and deployment of network information assets through security policies to ensure the security of network information assets. Among them, the security policy refers to a set of rules for all securit...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L29/06H04L12/24
CPCH04L41/0823H04L63/20H04L63/205
Inventor 刘博范渊杨锦峰聂桂兵
Owner HANGZHOU ANHENG INFORMATION TECH CO LTD
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