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SCIRD: Revealing Infection of Malicious Software in Edge Computing-Enabled IoT Networks

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Computers, Materials & Continua 2024年第5期79卷 2743-2769页

作者： Jiehao Ye Wen Cheng Xiaolong Liu Wenyi Zhu Xuan’ang Wu shigen shen School of Information Engineering Huzhou UniversityHuzhou313000China

The Internet of Things(IoT)has characteristics such as node mobility,node heterogeneity,link heterogeneity,and topology *** the face of the IoT characteristics and the explosive growth of IoT nodes,which brings about large-scale data processing requirements,edge computing architecture has become an emerging network architecture to support IoT applications due to its ability to provide powerful computing capabilities and good service ***,the defense mechanism of Edge Computing-enabled IoT Nodes(ECIoTNs)is still weak due to their limited resources,so that they are susceptible to malicious software spread,which can compromise data confidentiality and network service *** this situation,we put forward an epidemiology-based susceptible-curb-infectious-removed-dead(SCIRD)***,we analyze the dynamics of ECIoTNs with different infection levels under different initial conditions to obtain the dynamic differential ***,we establish the presence of equilibrium states in the SCIRD ***,we conduct an analysis of the model’s stability and examine the conditions under which malicious software will either spread or disappear within Edge Computing-enabled IoT(ECIoT)***,we validate the efficacy and superiority of the SCIRD model through MATLAB *** research findings offer a theoretical foundation for suppressing the propagation of malicious software in ECIoT *** experimental results indicate that the theoretical SCIRD model has instructive significance,deeply revealing the principles of malicious software propagation in ECIoT *** study solves a challenging security problem of ECIoT networks by determining the malicious software propagation threshold,which lays the foundation for buildingmore secure and reliable ECIoT networks.

关键词： Edge computing Internet of Things malicious software propagation model heterogeneity

A Deep Neural Collaborative Filtering Based Service Recommendation Method with Multi-Source Data for Smart Cloud-Edge Collaboration Applications

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Tsinghua Science and Technology 2024年第3期29卷 897-910页

作者： Wenmin Lin Min Zhu Xinyi Zhou Ruowei Zhang Xiaoran Zhao shigen shen Lu Sun Alibaba Business School Hangzhou Normal UniversityHangzhou 311121China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and TechnologyShouguang 262700China School of Computer Science Qufu Normal UniversityRizhao 276827China School of Information Engineering Huzhou UniversityHuzhou 313000China

Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user ***,the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation *** view of these challenges,we propose a deep neural collaborative filtering based service recommendation method with multi-source data(i.e.,NCF-MS)in this paper,which adopts the cloud-edge collaboration computing paradigm to build recommendation *** specifically,the Stacked Denoising Auto Encoder(SDAE)module is adopted to extract user/service features from auxiliary user profiles and service *** Multiple Layer Perceptron(MLP)module is adopted to integrate the auxiliary user/service features to train the recommendation ***,we evaluate the effectiveness of the NCF-MS method on three public *** experimental results show that our proposed method achieves better performance than existing methods.

关键词： deep neural collaborative filtering multi-source data cloud-edge collaboration application stackeddenoising auto encoder multiple layer perceptron

基于微分博弈的异质无线传感器网络恶意程序传播研究与分析

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计算机应用与软件 2024年第7期41卷 100-105页

作者：汤梦晨吴国文张红沈士根曹奇英东华大学计算机科学与技术学院上海201620 绍兴文理学院计算机科学与工程系浙江绍兴312000

为抑制异质无线传感器网络(Heterogeneous Wireless Sensor Networks,HWSNs)恶意程序的传播,考虑节点能量和计算能力差异,提出一种HWSNs攻防博弈模型。通过计算得到攻防双方的混合纳什均衡,再结合微分博弈建立攻防双方节点转换微分方程... 详细信息

为抑制异质无线传感器网络(Heterogeneous Wireless Sensor Networks,HWSNs)恶意程序的传播,考虑节点能量和计算能力差异,提出一种HWSNs攻防博弈模型。通过计算得到攻防双方的混合纳什均衡,再结合微分博弈建立攻防双方节点转换微分方程,分析得到恶意节点比例动态演化规律。结合数值实验分析,验证有效抑制HWSNs恶意程序传播的方式。

关键词：异质无线传感器网络微分博弈恶意程序入侵检测

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基于信号博弈的物联网隐私保护研究

计算机应用与软件 2024年第7期41卷 263-269,278页

作者：和青青吴国文张红沈士根曹奇英东华大学计算机科学与技术学院上海201620 绍兴文理学院计算机科学与工程系浙江绍兴312000

针对具有恶意节点攻击的物联网,提出一种基于信号博弈的隐私保护模型。引入信任度、攻击致命度等参数来构建更为完备的支付矩阵,计算博弈双方的收益,并根据贝叶斯原理,分析多阶段动态博弈中的贝叶斯均衡,寻求最优节点检测策略与攻击策... 详细信息

针对具有恶意节点攻击的物联网,提出一种基于信号博弈的隐私保护模型。引入信任度、攻击致命度等参数来构建更为完备的支付矩阵,计算博弈双方的收益,并根据贝叶斯原理,分析多阶段动态博弈中的贝叶斯均衡,寻求最优节点检测策略与攻击策略并研究不同策略对于物联网隐私保护的影响。结合仿真实验,验证不同因素对于物联网隐私保护的影响,提出提高物联网隐私保护程度的方法。

关键词：信号博弈物联网恶意节点贝叶斯均衡隐私保护

Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

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数字通信与网络:英文版 2023年第4期9卷 906-919页

作者： Yizhou shen shigen shen Qi Li Haiping Zhou Zongda Wu Youyang Qu Department of Computer Science and Engineering Shaoxing UniversityShaoxing312000China School of Computer Science and Informatics Cardiff UniversityCardiffCF243AAUnited Kingdom School of Information Engineering Huzhou UniversityHuzhou 313000ZhejiangChina School of Information Technology Deakin UniversityBurwoodVIC3125Australia

The fast proliferation of edge devices for the Internet of Things(IoT)has led to massive volumes of data *** generated data is collected and shared using edge-based IoT structures at a considerably high ***,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 *** address the identified issue,we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing *** 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 *** devices may make malicious requests to achieve their goals of stealing ***,edge nodes should deny malicious IoT device requests to prevent IoT data from being *** dynamically adjust their own strategies according to the opponent's strategy and finally maximize the *** upon a developed application framework to illustrate the concrete data sharing architecture,a novel algorithm is proposed that can derive the optimal evolutionary learning ***,we numerically simulate evolutionarily stable strategies,and the final results experimentally verify the correctness of the IoT data sharing privacy preservation ***,the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared.

关键词： Privacy preservation Internet of things Evolutionary game Data sharing Edge computing

基于半马尔可夫过程的异质WSNs可用度评估

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计算机应用与软件 2024年第3期41卷 124-129,133页

作者：杨淑敏吴国文张红沈士根曹奇英东华大学计算机科学与技术学院上海201620 绍兴文理学院计算机科学与工程系浙江绍兴312000

为评估异质无线传感器网络(HWSNs)的可用性,提出一种异质传感器节点状态更契合实际情况的SEQIRD(Susceptible Exposed Quarantined Infected Recovered Dead)新模型。使用半马尔可夫过程描述节点各状态之间的动态转换过程,得到各个状态... 详细信息

为评估异质无线传感器网络(HWSNs)的可用性,提出一种异质传感器节点状态更契合实际情况的SEQIRD(Susceptible Exposed Quarantined Infected Recovered Dead)新模型。使用半马尔可夫过程描述节点各状态之间的动态转换过程,得到各个状态的稳态概率,并计算得出不同拓扑结构下整个HWSNs稳态可用度。最后进行数值分析,以验证初始感染率、感染变化率和恢复率对HWSNs稳态可用度的影响。

关键词：无线传感器网络半马尔可夫过程异质可用度

A Spider Monkey Optimization Algorithm Combining Opposition-Based Learning and Orthogonal Experimental Design

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Computers, Materials & Continua 2023年第9期76卷 3297-3323页

作者： Weizhi Liao Xiaoyun Xia Xiaojun Jia shigen shen Helin Zhuang Xianchao Zhang College of Information Science and Engineering Jiaxing UniversityJiaxing314001China School of Information Engineering Huzhou UniversityHuzhou313000China Technology Research and Development Centre Xuelong Group Co.Ltd.Ningbo315899China School of Internet Jiaxing Vocational and Technical CollegeJiaxing314036China

As a new bionic algorithm,Spider Monkey Optimization(SMO)has been widely used in various complex optimization problems in recent ***,the new space exploration power of SMO is limited and the diversity of the population in SMO is not ***,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 ***,an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal ***,a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in ***,an extended SMO^(3)named CSMO^(3)is investigated to deal with constrained optimization *** proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering *** 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.

关键词： Spider monkey optimization opposition-based learning orthogonal experimental design particle swarm

FedNRM:A Federal Personalized News Recommendation Model Achieving User Privacy Protection

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Intelligent Automation & Soft Computing 2023年第8期37卷 1729-1751页

作者： Shoujian Yu Zhenchi Jie Guowen Wu Hong Zhang shigen shen School of Computer Science and Technology Donghua UniversityShanghai201620China School of Information Engineering Huzhou UniversityHuzhou313000China

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 ***,existing user models usually only consider users’long-term interests and ignore users’recent interests,which affects users’usage ***,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 *** 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 ***,we achieve a more accurate user *** also consider the risk of compromising user privacy if the user model training is placed on the server *** 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 *** further employ secure multiparty computation to request news representations from the server side,which protects privacy to some *** experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation *** 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@*** the same time,because we use a federated learning framework,compared with other centralized news recommendation methods,we achieve privacy protection for users.

关键词： News recommendation federal learning privacy protection personalized attention

Optimal Control of Heterogeneous-Susceptible-Exposed-Infectious-Recovered-Susceptible Malware Propagation Model in Heterogeneous Degree-Based Wireless Sensor Networks

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Journal of Donghua University(English Edition) 2022年第3期39卷 220-230页

作者：张红沈士根吴国文曹奇英许洪云 School of Computer Science and Technology Donghua University Shanghai 201620 China Department of Computer Science and Engineering Shaoxing University Shaoxing 312000 China Faculty of Business Information Shanghai Business School Shanghai 201400 China

Heterogeneous wireless sensor networks(HWSNs)are vulnerable to malware propagation,because of their low configuration and weak defense ***,an optimality system for HWSNs is developed to suppress malware propagation in this ***,a heterogeneous-susceptible-exposed-infectious-recovered-susceptible(HSEIRS)model is proposed to describe the state dynamics of heterogeneous sensor nodes(HSNs)in ***,the existence of an optimal control problem with installing antivirus on HSNs to minimize the sum of the cumulative infection probabilities of HWSNs at a low cost based on the HSEIRS model is proved,and then an optimal control strategy for the problem is derived by the optimal control ***,the optimal control strategy based on the HSEIRS model is transformed into corresponding Hamiltonian by the Pontryagin’s minimum principle,and the corresponding optimality system is ***,the effectiveness of the optimality system is validated by the experimental simulations,and the results show that the infectious HSNs will fall to an extremely low level at a low cost.

关键词： heterogeneous wireless sensor network(HWSN) malware propagation optimal control Pontryagin’s minimum principle