Fraud website identification method and system based on picture clustering and artificial study and judgment

A recognition method and image technology, applied in character and pattern recognition, website content management, network data retrieval, etc., can solve problems such as inability to identify new types of fraudulent websites, lack of discovery of new fraudulent websites, etc., to improve representativeness and reliability , Improve recognition ability, improve the effect of discovery ability

Active Publication Date: 2022-02-15
成都无糖信息技术有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] What exists in the existing technology is only the analysis and modeling of the existing fraudulent websites. Learning from the characteristics of the existing fraudulent websites can only identify the fraudulent websites related to the existing fraudulent websites, and lacks the discovery of new fraudulent websites. As well as the problem that the types of new fraudulent websites cannot be quickly and effectively discriminated, the present invention proposes a method and system for identifying fraudulent websites based on image clustering and manual research and judgment. Perform feature matching and analysis on unknown websites to discover new fraud-related websites and new types of fraud-related websites

Method used

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  • Fraud website identification method and system based on picture clustering and artificial study and judgment

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0050] 1. Extract feature vectors and hash feature values ​​from known fraud-related websites, and enter them into the fraud-related sample database.

[0051] 2. Calculate the Euclidean distance on the feature vectors of the fraud sample database in a recursive manner, and perform clustering based on the Euclidean distance.

[0052] 3. For a batch of unknown websites, the image feature vectors of the websites are obtained through the feature model.

[0053] 4. Calculate the Euclidean distance between the image feature vector and each type of cluster center feature vector of the fraudulent sample library, and judge the type of the unknown website by the distance. If the distance is 0, it is directly judged that the website is an illegal website. For those whose distance is not 0, it is regarded as a suspected illegal website, and the matching type is regarded as a suspected type.

[0054] 5. Manually analyze the suspected types obtained from this batch of unknown websites, and...

Embodiment 2

[0059] Such as figure 1 As shown, a fraudulent website identification method based on image clustering and manual judgment is provided, including:

[0060] S1: Obtain effective website screenshots of existing fraudulent websites and their fraudulent types, perform feature extraction on fraudulent images, obtain image fusion features as fraud samples, and classify according to the fraudulent types of image fusion features; S1 is specifically:

[0061] S1.1. Obtain existing known fraudulent websites and their types.

[0062] S1.2. Build a model based on the mobilenetv3 network and pre-training weights based on the imageNet dataset.

[0063] S1.3. Based on the constructed pre-training model combined with the existing data of fraudulent websites, transfer learning is performed to learn the feature distribution of fraudulent websites.

[0064] S1.4. Use a transfer learning model that fits the distribution of fraudulent websites as a feature extractor.

[0065] S1.5. Obtain the g...

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Abstract

The invention discloses a fraud website identification method and system based on picture clustering and artificial study and judgment, belongs to the technical field of image processing, and aims to solve the problems that in the prior art, discovery of novel fraud websites is lacked, and the novel fraud websites cannot be effectively supplemented for existing fraud-related sample data. According to the technical scheme, the method comprises the following steps: obtaining an existing fraud website and a fraud-related type thereof, and performing feature extraction to obtain a picture fusion feature; inputting all the picture fusion features and the fraud-related types into a fraud-related sample library, and obtaining a clustering center feature vector of each fraud-related type; acquiring a picture fusion feature of an unknown website, and then matching the picture fusion feature with a fraud-related sample library to obtain a suspected fraud-related type of the unknown picture; and analyzing a batch of unknown websites with suspected fraud-related types to obtain novel fraud-related types and novel fraud-related websites which meet conditions.

Description

technical field [0001] The invention belongs to the technical field of picture information processing, and in particular relates to a fraudulent website identification method and system based on picture clustering and manual research and judgment. Background technique [0002] With the continuous development of the information age, the Internet has become a new gathering place for scammers. Due to the difficulty of network supervision, people's awareness of network security prevention is relatively low, and new types of network fraud methods are full of tricks, which has led to communication network fraud cases in recent years. Frequent occurrences have brought serious life troubles and economic losses to netizens, and also caused adverse effects on society. Therefore, effective identification of fraudulent websites is crucial. [0003] In the prior art, fraudulent websites are identified by collecting existing fraudulent website data, and then identifying fraudulent website...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V10/74G06V10/762G06V10/764G06V10/80G06K9/62G06F16/958
CPCG06F16/958G06F18/23G06F18/22G06F18/24G06F18/253
Inventor 漆伟张瑞冬马永霄童永鳌朱鹏张浩
Owner 成都无糖信息技术有限公司
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