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An immunization framework for social networks through big data based influence modeling

journal contribution
posted on 2019-11-01, 00:00 authored by S Peng, G Wang, Y Zhou, C Wan, C Wang, Shui Yu
IEEE Social networks are critical in terms of information or malware propagation. However, how to contain the spreading of malware in social networks is still an open and challenging issue. In this paper, we propose a novel defending method through big data based influence modeling. We first establish a social interaction graph based on big data sets of the studied object. Based on the graph, we are able to measure direct influence of individuals by computing each node & #x0027;s strength, which includes the degree of the node and the total number of messages sent by each user to her friends. Then, we design an algorithm to construct influence spreading tree using the breadth first search strategy, and measure indirect influence of individuals by traversing the tree. We identify the top k influential nodes among all the nodes via the social influence strength, and propose an immunization algorithm to defend social networks against various attacks. The extensive experiments show that influence can spread easily in social networks, and the greater the influence of initial spread node is, the more impact it is on the malware propagation in social networks. The proposed method provides an effective solution to the prevention of malware or malicious messages propagation in social networks.

History

Journal

IEEE transactions on dependable and secure computing

Volume

16

Issue

6

Season

November/December

Pagination

984 - 995

Publisher

Institute of Electrical and Electronics Engineers

Location

Piscataway, N.J.

ISSN

1545-5971

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2017, IEEE