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Dark network threat prediction system based on machine learning

A technology of machine learning and prediction system, which is applied in the field of dark web threat prediction system based on machine learning, and can solve problems such as harm

Inactive Publication Date: 2021-01-08
SHANGHAI JIAO TONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The present in this technology allows users to easily scan and view hidden web pages without having them stop working or being annoyed by irrelevant details from their browsing sessions. It also helps predict potential security threats based upon these URLs' contents. This improves efficiency when managing network resources while ensuring safety measures are taken correctly.

Problems solved by technology

Technician Examines Different Problem: While searching for internet connections over the past few years, finding weaknesses like those caused by technical problem addressed in previous research works involves identifying potential sources of attacks related to darknesswebs. However current systems have limitations when trying to identify them due to their lack of ability to accurately predict how well future attack scenarios may occur without requiring detailed knowledge of everything involved.

Method used

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  • Dark network threat prediction system based on machine learning
  • Dark network threat prediction system based on machine learning

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Embodiment Construction

[0034] The following describes several preferred embodiments of the present invention with reference to the accompanying drawings, so as to make the technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned herein.

[0035] In the drawings, components with the same structure are denoted by the same numerals, and components with similar structures or functions are denoted by similar numerals. The size and thickness of each component shown in the drawings are shown arbitrarily, and the present invention does not limit the size and thickness of each component. In order to make the illustration clearer, the thickness of parts is appropriately exaggerated in some places in the drawings.

[0036] like figure 1 As shown, this embodiment includes a data crawling module, a data storage and display module, a data classifi...

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Abstract

The invention discloses a dark network threat prediction system based on machine learning, and relates to the field of computer network security, the dark network threat prediction system comprises adata crawling module, a data storage and display module, a data classification module and a vulnerability prediction module, and the data crawling module realizes high-concurrency crawling and monitoring of dark network data; the data storage and display module realizes data storage and full-text search, displays data overall distribution and classification conditions on a web page, and comprisesa data storage unit, a data search unit and a data display unit; the data classification module uses an unsupervised method to automatically search for optimal parameters and extract features, data classification is realized, and a classification result is recorded; and the vulnerability prediction module uses a vulnerability data screening algorithm to screen out data containing vulnerability related information for modeling to obtain utilized vulnerability IDs and related information. According to the invention, vulnerability utilization information can be discovered in time, and a manufacturer is reminded to take corresponding protection measures.

Description

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Claims

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Application Information

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Owner SHANGHAI JIAO TONG UNIV
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