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A detection method for pan attack based on stacked sparse autoencoder

A sparse autoencoder and attack detection technology, which is applied in the field of information security, can solve problems such as difficult to deal with trust attacks

Active Publication Date: 2018-06-12
XIDIAN UNIV
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AI Technical Summary

Problems solved by technology

However, in the traditional classified trolling attack detection algorithm, it is difficult to deal with more types of trolling attacks

Method used

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  • A detection method for pan attack based on stacked sparse autoencoder
  • A detection method for pan attack based on stacked sparse autoencoder
  • A detection method for pan attack based on stacked sparse autoencoder

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Embodiment Construction

[0021] refer to figure 1 , the implementation steps of the present invention are as follows:

[0022] Step 1, input scoring dataset.

[0023] The scoring data set is divided into a large number of unknown types of users and a small number of known types of users, both of which include trusted users and normal users, and the entire scoring data is R=|D|×|I|, where R refers to the scale of The rating matrix of D|×|I|, D refers to all users, I refers to all items, |D| and |I| refer to the number of D and I respectively, and D=D U ∪D 1 ∪…∪D q ∪…∪D c , 1≤q≤c, where D U refers to a collection of users of unknown type, D q refers to the known set of q-type users, and c refers to the total number of known user types;

[0024] Step 2, normalize the scoring matrix R.

[0025] When users evaluate items, since the scoring scales of each user are different, for example, for their favorite items, some users will give full marks, while some users will only give average marks, which w...

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Abstract

The invention discloses a trolling attack detection method based on a stacked sparse autoencoder, which mainly solves the problem in the prior art that users need to extract corresponding features for different types of trolling attacks. The implementation steps are: (1) Input the initial rating data set; (2) Initialize the initial rating data set; (3) Directly use the rating of each user as input to train the stacked sparse autoencoder to extract the user's feature data; (4) Use the extracted feature data as input to train a Naive Bayesian classifier; (5) Calculate the probability that users of unknown types belong to each type according to the trained Naive Bayesian classifier, and find out the attacking users. The invention can directly use the stacked sparse self-encoder to extract the characteristic data of each user, can stably detect various types of trolling attacking users, and can be used to detect malicious attacking users in the Internet system.

Description

technical field [0001] The invention belongs to the field of information security, and in particular relates to a troll attack detection method, which can be used to detect malicious attack users in an Internet system. Background technique [0002] At the end of the 20th century, the Internet began to sprout. With the rapid growth of the amount of information on the Internet, it takes a lot of energy for people to find the information they need online. It is this demand that gave birth to the establishment of Yahoo! Portals bring well-being to people. However, it didn't take long before the amount of information in the whole society began to grow exponentially, and it became difficult for portal websites to handle such a large-scale content. Google came into being, and the search business perfectly solved the problem of finding the content you need in massive amounts of information, and began its golden period of development. Entering the 21st century, with the further pop...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F21/55G06K9/62
CPCG06F21/552G06F2221/034G06F18/24155
Inventor 马文萍马进焦李成马晶晶闻泽联任琛
Owner XIDIAN UNIV
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