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首页> 外文期刊>IEEE transactions on dependable and secure computing >An Immunization Framework for Social Networks Through Big Data Based Influence Modeling
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An Immunization Framework for Social Networks Through Big Data Based Influence Modeling

机译：基于大数据的影响力建模的社交网络免疫框架

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摘要

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 nodes 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 $k$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.
机译：社交网络对于信息或恶意软件传播至关重要。但是，如何控制社交网络中恶意软件的传播仍然是一个开放且具有挑战性的问题。在本文中，我们提出了一种基于大数据的影响建模的新颖防御方法。我们首先基于研究对象的大数据集建立一个社交互动图。基于该图，我们能够通过计算每个节点的强度来衡量个人的直接影响力，其中包括节点的程度以及每个用户发送给她的朋友的消息总数。然后，我们设计了一种算法，该算法使用广度优先搜索策略构造影响力传播树，并通过遍历树来测量个体的间接影响力。我们通过社交影响力确定所有节点中最重要的$ k $ k个影响节点，并提出一种免疫算法来防御社交网络遭受各种攻击。大量实验表明，影响力很容易在社交网络中传播，初始传播节点的影响越大，对恶意软件在社交网络中传播的影响就越大。所提出的方法为防止恶意软件或恶意消息在社交网络中传播提供了有效的解决方案。

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