With China's rapid economic progress and steady increase in its international influence, China has gradually embarked on the soft power idea and has made developing its soft power as its national strategy. We argue that China's soft power strategy is in accordance to Chinese Confucian culture and political value and fits well with its grand strategy of peaceful rise. Based on existing conceptualizations of soft power, we expanded the sources of soft power to six pillars: cultural attractiveness, political values, development model, international institutions, international image, and economic temptation. We also identified three channels for wielding soft power: formal, economic, and cultural diplomacies. Putting all the basics together, we present an integrative model of soft power. Accordingly, we analyze the sources and limits of China's soft power and suggest how to improve it.

As an important part of intrusion detection, feature selection plays a significant role in improving the performance of intrusion detection. Krill herd (KH) algorithm is an efficient swarm intelligence algorithm with excellent performance in data mining. To solve the problem of low efficiency and high false positive rate in intrusion detection caused by increasing high-dimensional data, an improved krill swarm algorithm based on linear nearest neighbor lasso step (LNNLS-KH) is proposed for feature selection of network intrusion detection. The number of selected features and classification accuracy are introduced into fitness evaluation function of LNNLS-KH algorithm, and the physical diffusion motion of the krill individuals is transformed by a nonlinear method. Meanwhile, the linear nearest neighbor lasso step optimization is performed on the updated krill herd position in order to derive the global optimal solution. Experiments show that the LNNLS-KH algorithm retains 7 features in NSL-KDD dataset and 10.2 features in CICIDS2017 dataset on average, which effectively eliminates redundant features while ensuring high detection accuracy. Compared with the CMPSO, ACO, KH, and IKH algorithms, it reduces features by 44%, 42.86%, 34.88%, and 24.32% in NSL-KDD dataset, and 57.85%, 52.34%, 27.14%, and 25% in CICIDS2017 dataset, respectively. The classification accuracy increased by 10.03% and 5.39%, and the detection rate increased by 8.63% and 5.45%. Time of intrusion detection decreased by 12.41% and 4.03% on average. Furthermore, LNNLS-KH algorithm quickly jumps out of the local optimal solution and shows good performance in the optimal fitness iteration curve, convergence speed, and false positive rate of detection.

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With the rapid development of network attacks, traditional security protection technology is difficult to deal with unknown threats and persistent attacks. Active defense improves the ability to defend against network attacks by building a dynamic, heterogeneous and redundant endogenous security system. Aiming at the problem of single abnormal arbitrament information of routers in mimic defense, a router abnormal traffic detection strategy based on active defense is proposed. By clustering the traffic information of multiple heterogeneous redundant routing function entities and comparing the distance measurement between them, the routing function entities in abnormal state are determined. The experimental results show that the proposed strategy effectively detects the security threats of routing functional entities and expand the method of mimic router arbitrament.