Community detection method for resisting Sybil attacks and used for large-scale network

Abstract

The invention discloses a community detection method for resisting Sybil attacks and used for a large-scale network, and the method comprises the steps: giving a social network G=(V, E), V being a node set, and E being a social link or a side set; giving a community link strength threshold t, and initializing a community link or an edge set C=null; calculating similarity indexes for nodes at two ends of the edges (u, v) in each set E, and recording the similarity indexes as suv; for each edge (u, v) in the set E, if suv is not less than t, adding the edge (u, v) into the set C; and calculatingall connected sub-graphs in the set C, wherein each connected sub-graph is a community. According to the invention, community detection of different granularities in a large-scale network can be realized, Sybil attacks can be effectively resisted, and the community security is improved.

Description

technical field [0001] The invention belongs to the technical field of cyberspace security community security, and in particular relates to a community detection method for large-scale networks against Sybil attacks. Background technique [0002] Community is an important feature of social networks, and is inherent in most social networks. Community detection detects the community existing in the network by observing and analyzing the social behavior and feature similarity between individuals. However, in reality, community relations on some networks are private information, and community detection will violate user privacy and cause security issues. For example, an AIDS patient often has more social relationships with his friends, encouraging and helping each other, but he does not want to reveal that he belongs to the AIDS community through social interaction, that is, community exposure. In addition, a friend of a patient may also have more social relationships with the...

AI Technical Summary

Problems solved by technology

Therefore, the cost of data perturbation is often high, or difficult to achieve;

[0006] 2. Cause new security issues

After the disturbance, the division of associations becomes chaotic, which is often used by illegal elements to deceive the associations, causing unpredictable security risks;

[0007] 3. Cannot resist Sybil attacks

Once an attacker establishes a social relationship with certain individuals, it is easy to fool the community detection method

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