Terrorist attacks are events which hinder the development of a region. Before the terrorist attacks, we need to conduct a graded evaluation of the terrorist attacks. After getting the level of terrorist attacks, we can fight terrorist organizations more effectively. This paper builds rating models for terrorist attacks, hidden or emerging terrorist organization classification discovery models, terrorist organization alliance network models and more, through quantitative research of the Global Terrorism Database, which solved the event classification. Through studying relevant literature and the variables of the Global Terrorism Database, this paper sorted out 25 observation variables related to the impact level (level of harm) of terrorist attacks. By establishing a mathematical model of factor analysis, 11 factors related to the impact level (level of harm) of terrorist attacks were constructed, and the variance of the contribution of each factor was used as the weight to calculate the comprehensive rate of the impact level of each terrorist attack. Finally, K-means clustering method is used to cluster and analyze the comprehensive rate of impact level, and the top 10 terrorist attacks with the highest impact level in the past two decades were obtained.

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Wireless sensor networks (WSNs) have become one of the core technologies of the internet of things (IoT) system. They are information generation and acquisition systems used by the IoT to sense and identify the surrounding environment. They are also sensor technology, embedding computing technology, communication technology and important product in the development of Internet technology, which have made the whole society more intelligent and humanized. WSNs are multi-hop self-organizing networks consisting of a large number of micro-sensor nodes deployed in the monitoring area. They can collaboratively sense, collect and process the monitored objects and transmit them to the observers. In this paper, we use the cascade failure method to find the key nodes in the WSNs. First, a complex network cascade failure model based on load redistribution is proposed. Differences from the existing model are as follows: (1) for each node, an overload function is defined; (2) the evolution of the network topology is replaced by node weight evolution. Based on the cascade failure model, a method for evaluating the importance of complex load network nodes is proposed and a new definition of node importance is given. This method helps to discover some potential “critical nodes” in the network. The final experimental analysis verifies the effectiveness and feasibility of the proposed method.

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