Visible to the public Biblio

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Li, Kun, Wang, Rui, Li, Haiwei, Hao, Yan.  2021.  A Network Attack Blocking Scheme Based on Threat Intelligence. 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP). :976–980.
In the current network security situation, the types of network threats are complex and changeable. With the development of the Internet and the application of information technology, the general trend is opener. Important data and important business applications will face more serious security threats. However, with the development of cloud computing technology, the trend of large-scale deployment of important business applications in cloud centers has greatly increased. The development and use of software-defined networks in cloud data centers have greatly reduced the effect of traditional network security boundary protection. How to find an effective way to protect important applications in open multi-step large-scale cloud data centers is a problem we need to solve. Threat intelligence has become an important means to solve complex network attacks, realize real-time threat early warning and attack tracking because of its ability to analyze the threat intelligence data of various network attacks. Based on the research of threat intelligence, machine learning, cloud central network, SDN and other technologies, this paper proposes an active defense method of network security based on threat intelligence for super-large cloud data centers.
Hu, Guangjun, Li, Haiwei, Li, Kun, Wang, Rui.  2021.  A Network Asset Detection Scheme Based on Website Icon Intelligent Identification. 2021 Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS). :255–257.
With the rapid development of the Internet and communication technologies, efficient management of cyberspace, safe monitoring and protection of various network assets can effectively improve the overall level of network security protection. Accurate, effective and comprehensive network asset detection is the prerequisite for effective network asset management, and it is also the basis for security monitoring and analysis. This paper proposed an artificial intelligence algorithm based scheme which accurately identify the website icon and help to determine the ownership of network assets. Through experiments based on data set collected from real network, the result demonstrate that the proposed scheme has higher accuracy and lower false alarm rate, and can effectively reduce the training cost.
Han, Ying, Li, Kun, Ge, Fawei.  2019.  Multiple Fault Diagnosis for Sucker Rod Pumping Systems Based on Matter Element Analysis with F-statistics. 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS). :66–70.
Dynamometer cards can reflect different down-hole working conditions of sucker rod pumping wells. It has great significances to realize multiple fault diagnosis for actual oilfield production. In this paper, the extension theory is used to build a matter-element model to describe the fault diagnosis problem of the sucker rod pumping wells. The correlation function is used to calculate the correlation degree between the diagnostic fault and many standard fault types. The diagnosed sample and many possible fault types are divided into different combinations according to the correlation degree; the F-statistics of each combination is calculated and the “unbiased transformation” is used to find the mean of interval vectors. Larger F-statistics means greater differences within the faults classification; and the minimum F-statistics reflects the real multiple fault types. Case study shows the effectiveness of the proposed method.