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Depth study-based network risk early warning method

A deep learning and risk early warning technology, applied in the field of network risk early warning based on machine deep learning, can solve problems such as the inability to quickly and comprehensively obtain security status, improve processing efficiency and accuracy, save time, and improve response speed Effect

Active Publication Date: 2017-08-29
赖洪昌
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] In the current network security field, traditional methods such as vulnerability scanning and port scanning are used to detect whether a target is safe or not. This method is effective for a single target with obvious vulnerabilities, but cannot be quickly used for batch targets or targets without obvious vulnerabilities. Comprehensive access to its security status

Method used

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  • Depth study-based network risk early warning method

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Embodiment Construction

[0018] The present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be emphasized that the following descriptions are only exemplary and not intended to limit the scope of the present invention and its application.

[0019] Such as figure 1 As shown, this embodiment provides a network risk early warning method based on deep learning, which includes the following steps:

[0020] Step 1. Collect sample data of cyberspace asset risk in the entire network segment.

[0021] Step 1-1, build a database of cyberspace asset risk sample data, identify risk points of assets, and determine risk factors. Risk factors include: target IP, open ports, server system type and version, server application type and version, Existing vulnerabilities, database type and version, weak passwords, whether CDN acceleration is used, and whether a firewall is used. According to the risk factors, the cyb...

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PUM

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Abstract

The invention discloses a depth study-based network risk early warning method. The method comprises the following steps of: A1. collecting cyberspace asset risk sample data of a full network segment, and storing the cyberspace asset risk sample data in a database; A2. extracting data from the database, and performing convolutional neural network distributed training study, so as to form an initial risk prediction model; and A3. inputting production data into the risk prediction model, evaluating a risk value of the production data, if the risk value reaches an early warning threshold, alarming. Through adoption of the method and device, security risk evaluation and early warning are performed on multiple target networks or targets without obvious vulnerability, and the security state of one network can be evaluated overall; the response speed is improved, and the risk points can be found rapidly; and the maintenance cost is lowered, and the manpower is saved.

Description

technical field [0001] The present invention relates to network risk early warning technology, in particular to a network risk early warning method and system based on machine deep learning within a regional scope. Background technique [0002] In the current network security field, traditional methods such as vulnerability scanning and port scanning are used to detect whether a target is safe or not. This method is effective for a single target with obvious vulnerabilities, but it cannot be quickly used for batch targets or targets without obvious vulnerabilities. Comprehensive access to its security status. Contents of the invention [0003] In order to solve the above problems, the present invention provides a network risk early warning method and device based on deep learning, which can quickly and comprehensively obtain the security status of batch targets or targets without obvious loopholes. [0004] The present invention provides a network risk early warning metho...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/06G06F17/30
CPCG06F16/245H04L63/20
Inventor 赖洪昌
Owner 赖洪昌
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