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| 1 | Task scheduling optimization in cloud computing based on heuristic algorithm 显示文摘 | Guo Lizheng Zhao Shuguang Shen Shigen | 2012 | Journal of Networks2012，7，3： | 1 |
| 2 | Differential Game-based Strategies for Preventing Malware Propagation in Wireless Sensor Networks 显示文摘 | Shen Shigen Li Hongjie Han Risheng | 2014 | IEEE Transactions on Information Forensics & Security2014，9，11： | 1 |
| 3 | Task schedu- ling optimization in cloud computing based on heuristic algo- rithm 显示文摘 | Guo Lizheng Zhao Shuguang Shen Shigen | 2012 | Journal of Networks2012，7，3： | 1 |
| 4 | Signaling game based strategy of intrusion detection in wireless sensor networks显示文摘 | Shigen Shen Yuanjie Li Hongyun Xu Qiying Cao | 2011 | Computers and Mathematics with Applications2011，，： | 1 |
| 5 | FedNRM:A Federal Personalized News Recommendation Model Achieving User Privacy Protection显示文摘In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the candidate news,and finally recommend the news with high scores to users.However,existing user models usually only consider users’long-term interests and ignore users’recent interests,which affects users’usage experience.Therefore,this paper introduces gated recurrent unit(GRU)sequence network to capture users’short-term interests and combines users’short-term interests and long-terminterests to characterize users.While existing models often only use the user’s browsing history and ignore the variability of different users’interest in the same news,we introduce additional user’s ID information and apply the personalized attention mechanism for user representation.Thus,we achieve a more accurate user representation.We also consider the risk of compromising user privacy if the user model training is placed on the server side.To solve this problem,we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side.We further employ secure multiparty computation to request news representations from the server side,which protects privacy to some extent.Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics.Compared with the current state-of-the-art federated news recommendation models,our model has increased by 0.54%in AUC,1.97%in MRR,2.59%in nDCG@5%,and 1.89%in nDCG@10.At the same time,because we use a federated learning framework,compared with other centralized news recommendation methods,we achieve privacy protection for users. | Shoujian Yu Zhenchi Jie Guowen Wu Hong Zhang Shigen Shen | 2023 | Intelligent Automation & Soft Computing2023，37，8： | 0 |
| 6 | Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes显示文摘The fast proliferation of edge devices for the Internet of Things(IoT)has led to massive volumes of data explosion.The generated data is collected and shared using edge-based IoT structures at a considerably high frequency.Thus,the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge nodes.To address the identified issue,we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing scheme.In particular,we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes,where IoT devices and edge nodes are two parties of the game.IoT devices may make malicious requests to achieve their goals of stealing privacy.Accordingly,edge nodes should deny malicious IoT device requests to prevent IoT data from being disclosed.They dynamically adjust their own strategies according to the opponent's strategy and finally maximize the payoffs.Built upon a developed application framework to illustrate the concrete data sharing architecture,a novel algorithm is proposed that can derive the optimal evolutionary learning strategy.Furthermore,we numerically simulate evolutionarily stable strategies,and the final results experimentally verify the correctness of the IoT data sharing privacy preservation scheme.Therefore,the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared. | Yizhou Shen Shigen Shen Qi Li Haiping Zhou Zongda Wu Youyang Qu | 2023 | Digital Communications and Networks2023，9，4： | 0 |
| 7 | A Spider Monkey Optimization Algorithm Combining Opposition-Based Learning and Orthogonal Experimental Design显示文摘As a new bionic algorithm,Spider Monkey Optimization(SMO)has been widely used in various complex optimization problems in recent years.However,the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant.Thus,this paper focuses on how to reconstruct SMO to improve its performance,and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design(SMO^(3))is developed.A position updatingmethod based on the historical optimal domain and particle swarmfor Local Leader Phase(LLP)andGlobal Leader Phase(GLP)is presented to improve the diversity of the population of SMO.Moreover,an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal values.Also,a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in time.Furthermore,an extended SMO^(3)named CSMO^(3)is investigated to deal with constrained optimization problems.The proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering problems.Experimental results show that the performance of the proposed algorithm is better than three well-known SMO algorithms and other evolutionary algorithms in unconstrained and constrained problems. | Weizhi Liao Xiaoyun Xia Xiaojun Jia Shigen Shen Helin Zhuang Xianchao Zhang | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
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