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| 1 | BMP-6 inhibits microRNA-21 expression in breast cancer through repressing 6EF1 and AP-1显示文摘 | Jun Du Shuang Yang Di An Fen Hu Wei Yuan Chunli Zhai Tianhui Zhu | 2009 | Cell Research2009，19，4： | 44 |
| 2 | Hydrological simulation by SWAT model with fixed and varied parameterization approaches under land use change显示文摘 | Du Jinkang Rui Hanyi Zuo Tianhui | 2013 | Water Resour Manage2013，，27： | 1 |
| 3 | Family medicine education and training in China:Past,present and future显示文摘 | Tianhui Chen Yaping Du Alex Sohal | | 0，，541： | 1 |
| 4 | Family medicine education and training in china显示文摘 | Tianhui Chen Yaping Du Alex Sohal etl | 2007 | British Journal of General Practice2007，，8： | 1 |
| 5 | Application Research on Two-Layer Threat Prediction Model Based on Event Graph显示文摘Advanced Persistent Threat(APT)is now the most common network assault.However,the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks.They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats.To address the above problems,firstly,this paper constructs the multi-source threat element analysis ontology(MTEAO)by integrating multi-source network security knowledge bases.Subsequently,based on MTEAO,we propose a two-layer threat prediction model(TL-TPM)that combines the knowledge graph and the event graph.The macro-layer of TL-TPM is based on the knowledge graph to derive the propagation path of threats among devices and to correlate threat elements for threat warning and decision-making;The micro-layer ingeniously maps the attack graph onto the event graph and derives the evolution path of attack techniques based on the event graph to improve the explainability of the evolution of threat events.The experiment’s results demonstrate that TL-TPM can completely depict the threat development trend,and the early warning results are more precise and scientific,offering knowledge and guidance for active defense. | Shuqin Zhang Xinyu Su Yunfei Han Tianhui Du Peiyu Shi | 2023 | Computers, Materials & Continua2023，77，12： | 0 |
| 6 | Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge显示文摘Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment. | Shuqin Zhang Xinyu Su Peiyu Shi Tianhui Du Yunfei Han | 2023 | Computers, Materials & Continua2023，77，10： | 0 |
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