Visible to the public Biblio

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2018
He, F., Zhang, Y., Liu, H., Zhou, W..  2018.  SCPN-Based Game Model for Security Situational Awareness in the Intenet of Things. 2018 IEEE Conference on Communications and Network Security (CNS). :1-5.
Internet of Things (IoT) is characterized by various of heterogeneous devices that facing numerous threats, which makes modeling security situation of IoT still a certain challenge. This paper defines a Stochastic Colored Petri Net (SCPN) for IoT-based smart environment and then proposes a Game model for security situational awareness. All possible attack paths are computed by the SCPN, and antagonistic behavior of both attackers and defenders are taken into consideration dynamically according to Game Theory (GT). Experiments on two typical attack scenarios in smart home environment demonstrate the effectiveness of the proposed model. The proposed model can form a macroscopic trend curve of the security situation. Analysis of the results shows the capabilities of the proposed model in finding vulnerable devices and potential attack paths, and even facilitating the choice of defense strategy. To the best of our knowledge, this is the first attempt to use Game Theory in the IoT-based SCPN to establish a security situational awareness model for a complex smart environment.
2020
Li, Y., Zhou, W., Wang, H..  2020.  F-DPC: Fuzzy Neighborhood-Based Density Peak Algorithm. IEEE Access. 8:165963–165972.
Clustering is a concept in data mining, which divides a data set into different classes or clusters according to a specific standard, making the similarity of data objects in the same cluster as large as possible. Clustering by fast search and find of density peaks (DPC) is a novel clustering algorithm based on density. It is simple and novel, only requiring fewer parameters to achieve better clustering effect, without the requirement for iterative solution. And it has expandability and can detect the clustering of any shape. However, DPC algorithm still has some defects, such as it employs the clear neighborhood relations to calculate local density, so it cannot identify the neighborhood membership of different values of points from the distance of points and It is impossible to accurately cluster the data of the multi-density peak. The fuzzy neighborhood density peak clustering algorithm is proposed for this shortcoming (F-DPC): novel local density is defined by the fuzzy neighborhood relationship. The fuzzy set theory can be used to make the fuzzy neighborhood function of local density more sensitive, so that the clustering for data set of various shapes and densities is more robust. Experiments show that the algorithm has high accuracy and robustness.
Si, Y., Zhou, W., Gai, J..  2020.  Research and Implementation of Data Extraction Method Based on NLP. 2020 IEEE 14th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :11–15.
In order to accurately extract the data from unstructured Chinese text, this paper proposes a rule-based method based on natural language processing and regular expression. This method makes use of the language expression rules of the data in the text and other related knowledge to form the feature word lists and rule template to match the text. Experimental results show that the accuracy of the designed algorithm is 94.09%.