The Experts below are selected from a list of 45 Experts worldwide ranked by ideXlab platform
Qiying Cao - One of the best experts on this subject based on the ideXlab platform.
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Modeling and analyzing Malware Diffusion in wireless sensor networks based on cellular automaton
International Journal of Distributed Sensor Networks, 2020Co-Authors: Hong Zhang, Shigen Shen, Qiying Cao, Shaofeng LiuAbstract:Wireless sensor networks, as a multi-hop self-organized network system formed by wireless communication, are vulnerable to Malware Diffusion by breaking the data confidentiality and service availab...
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HSIRD: A model for characterizing dynamics of Malware Diffusion in heterogeneous WSNs
Journal of Network and Computer Applications, 2019Co-Authors: Shigen Shen, Haiping Zhou, Feng Sheng, Longjun Huang, Jianhua Liu, Qiying CaoAbstract:Abstract Heterogeneous wireless sensor networks (HWSNs), as blocks of the Internet of Things, are vulnerable to Malware Diffusion breaking the data confidentiality and service availability, owing to their weak defense mechanism and poor resilience. Thus, constructing a Malware Diffusion model and revealing the rules of Malware Diffusion in HWSNs are urgently needed. In this context, we propose a Heterogeneous Susceptible-Infectious-Removed-Dead (HSIRD) model based on epidemiology, in order to not only characterize the dead state where a heterogeneous sensor node (HSN) may lose its functionality owing to physical damage or Malware attacks but also represent the HSN communication connectivity, which is one of the heterogeneities that exist universally in HWSNs. We then analyze the dynamics of the fractions of HSNs belonging to different degrees in different states and obtain the corresponding differential equations. Using these equations, we prove the existence of equilibrium points of the HSIRD model. Subsequently, we attain the basic reproduction number governing the stability of the equilibrium points. We further prove the stability of the equilibrium points of the model and attain the conditions indicating whether Malware in HWSNs will diffuse or die out. Finally, we validate the effectiveness of the model via simulation. The results provide a theoretical foundation for suppressing Malware Diffusion in Malware-infected HWSNs.
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Multistage Signaling Game-Based Optimal Detection Strategies for Suppressing Malware Diffusion in Fog-Cloud-Based IoT Networks
IEEE Internet of Things Journal, 2018Co-Authors: Shigen Shen, Haiping Zhou, Longjun Huang, En Fan, Qiying CaoAbstract:We consider the Internet of Things (IoT) with Malware Diffusion and seek optimal Malware detection strategies for preserving the privacy of smart objects in IoT networks and suppressing Malware Diffusion. To this end, we propose a Malware detection infrastructure realized by an intrusion detection system (IDS) with cloud and fog computing to overcome the IDS deployment problem in smart objects due to their limited resources and heterogeneous subnetworks. We then employ a signaling game to disclose interactions between smart objects and the corresponding fog node because of Malware uncertainty in smart objects. To minimize privacy leakage of smart objects, we also develop optimal strategies that maximize Malware detection probability by theoretically computing the perfect Bayesian equilibrium of the game. Moreover, we analyze the factors influencing the optimal probability of a malicious smart object diffusing Malware, and factors influencing the performance of a fog node in determining an infected smart object. Finally, we present a framework to demonstrate a potential and practical application of suppressing Malware Diffusion in IoT networks.
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A non-cooperative non-zero-sum game-based dependability assessment of heterogeneous WSNs with Malware Diffusion
Journal of Network and Computer Applications, 2017Co-Authors: Shigen Shen, Jianhua Liu, En Fan, Qiying CaoAbstract:We consider Heterogeneous Wireless Sensor Networks (HWSNs) with Malware Diffusion and find a solution to assess their dependability in order to guarantee dependable operations on sending sensed data from sensor nodes (SNs) to a sink node. To this end, we regard an infection as a state transition of a Markov chain and propose a heterogeneous discrete-time Susceptible-Infected-Susceptible (SIS) model to disclose the Diffusion process by combining the SNs heterogeneity with the Malware's spread probability, which is foreseen by a developed non-cooperative non-zero-sum game. We further present reliability and availability measures for a susceptible SN from the perspective of reliability theory, from which we deduce and obtain metrics of reliability and availability assessment of HWSNs with star and cluster topologies. We therefore set up a dependability assessment mechanism for HWSNs with Malware Diffusion. Experiments illustrate the influence of the parameters on Malware's selection of the optimal spread probability and the mean time to infection of a susceptible SN. We also validate the effectiveness of the proposed mechanism. Our results can be applied to set up theoretical bases for governing the employment of reliable techniques.
Shigen Shen - One of the best experts on this subject based on the ideXlab platform.
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Modeling and analyzing Malware Diffusion in wireless sensor networks based on cellular automaton
International Journal of Distributed Sensor Networks, 2020Co-Authors: Hong Zhang, Shigen Shen, Qiying Cao, Shaofeng LiuAbstract:Wireless sensor networks, as a multi-hop self-organized network system formed by wireless communication, are vulnerable to Malware Diffusion by breaking the data confidentiality and service availab...
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HSIRD: A model for characterizing dynamics of Malware Diffusion in heterogeneous WSNs
Journal of Network and Computer Applications, 2019Co-Authors: Shigen Shen, Haiping Zhou, Feng Sheng, Longjun Huang, Jianhua Liu, Qiying CaoAbstract:Abstract Heterogeneous wireless sensor networks (HWSNs), as blocks of the Internet of Things, are vulnerable to Malware Diffusion breaking the data confidentiality and service availability, owing to their weak defense mechanism and poor resilience. Thus, constructing a Malware Diffusion model and revealing the rules of Malware Diffusion in HWSNs are urgently needed. In this context, we propose a Heterogeneous Susceptible-Infectious-Removed-Dead (HSIRD) model based on epidemiology, in order to not only characterize the dead state where a heterogeneous sensor node (HSN) may lose its functionality owing to physical damage or Malware attacks but also represent the HSN communication connectivity, which is one of the heterogeneities that exist universally in HWSNs. We then analyze the dynamics of the fractions of HSNs belonging to different degrees in different states and obtain the corresponding differential equations. Using these equations, we prove the existence of equilibrium points of the HSIRD model. Subsequently, we attain the basic reproduction number governing the stability of the equilibrium points. We further prove the stability of the equilibrium points of the model and attain the conditions indicating whether Malware in HWSNs will diffuse or die out. Finally, we validate the effectiveness of the model via simulation. The results provide a theoretical foundation for suppressing Malware Diffusion in Malware-infected HWSNs.
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Multistage Signaling Game-Based Optimal Detection Strategies for Suppressing Malware Diffusion in Fog-Cloud-Based IoT Networks
IEEE Internet of Things Journal, 2018Co-Authors: Shigen Shen, Haiping Zhou, Longjun Huang, En Fan, Qiying CaoAbstract:We consider the Internet of Things (IoT) with Malware Diffusion and seek optimal Malware detection strategies for preserving the privacy of smart objects in IoT networks and suppressing Malware Diffusion. To this end, we propose a Malware detection infrastructure realized by an intrusion detection system (IDS) with cloud and fog computing to overcome the IDS deployment problem in smart objects due to their limited resources and heterogeneous subnetworks. We then employ a signaling game to disclose interactions between smart objects and the corresponding fog node because of Malware uncertainty in smart objects. To minimize privacy leakage of smart objects, we also develop optimal strategies that maximize Malware detection probability by theoretically computing the perfect Bayesian equilibrium of the game. Moreover, we analyze the factors influencing the optimal probability of a malicious smart object diffusing Malware, and factors influencing the performance of a fog node in determining an infected smart object. Finally, we present a framework to demonstrate a potential and practical application of suppressing Malware Diffusion in IoT networks.
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A non-cooperative non-zero-sum game-based dependability assessment of heterogeneous WSNs with Malware Diffusion
Journal of Network and Computer Applications, 2017Co-Authors: Shigen Shen, Jianhua Liu, En Fan, Qiying CaoAbstract:We consider Heterogeneous Wireless Sensor Networks (HWSNs) with Malware Diffusion and find a solution to assess their dependability in order to guarantee dependable operations on sending sensed data from sensor nodes (SNs) to a sink node. To this end, we regard an infection as a state transition of a Markov chain and propose a heterogeneous discrete-time Susceptible-Infected-Susceptible (SIS) model to disclose the Diffusion process by combining the SNs heterogeneity with the Malware's spread probability, which is foreseen by a developed non-cooperative non-zero-sum game. We further present reliability and availability measures for a susceptible SN from the perspective of reliability theory, from which we deduce and obtain metrics of reliability and availability assessment of HWSNs with star and cluster topologies. We therefore set up a dependability assessment mechanism for HWSNs with Malware Diffusion. Experiments illustrate the influence of the parameters on Malware's selection of the optimal spread probability and the mean time to infection of a susceptible SN. We also validate the effectiveness of the proposed mechanism. Our results can be applied to set up theoretical bases for governing the employment of reliable techniques.
Jianhua Liu - One of the best experts on this subject based on the ideXlab platform.
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HSIRD: A model for characterizing dynamics of Malware Diffusion in heterogeneous WSNs
Journal of Network and Computer Applications, 2019Co-Authors: Shigen Shen, Haiping Zhou, Feng Sheng, Longjun Huang, Jianhua Liu, Qiying CaoAbstract:Abstract Heterogeneous wireless sensor networks (HWSNs), as blocks of the Internet of Things, are vulnerable to Malware Diffusion breaking the data confidentiality and service availability, owing to their weak defense mechanism and poor resilience. Thus, constructing a Malware Diffusion model and revealing the rules of Malware Diffusion in HWSNs are urgently needed. In this context, we propose a Heterogeneous Susceptible-Infectious-Removed-Dead (HSIRD) model based on epidemiology, in order to not only characterize the dead state where a heterogeneous sensor node (HSN) may lose its functionality owing to physical damage or Malware attacks but also represent the HSN communication connectivity, which is one of the heterogeneities that exist universally in HWSNs. We then analyze the dynamics of the fractions of HSNs belonging to different degrees in different states and obtain the corresponding differential equations. Using these equations, we prove the existence of equilibrium points of the HSIRD model. Subsequently, we attain the basic reproduction number governing the stability of the equilibrium points. We further prove the stability of the equilibrium points of the model and attain the conditions indicating whether Malware in HWSNs will diffuse or die out. Finally, we validate the effectiveness of the model via simulation. The results provide a theoretical foundation for suppressing Malware Diffusion in Malware-infected HWSNs.
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A non-cooperative non-zero-sum game-based dependability assessment of heterogeneous WSNs with Malware Diffusion
Journal of Network and Computer Applications, 2017Co-Authors: Shigen Shen, Jianhua Liu, En Fan, Qiying CaoAbstract:We consider Heterogeneous Wireless Sensor Networks (HWSNs) with Malware Diffusion and find a solution to assess their dependability in order to guarantee dependable operations on sending sensed data from sensor nodes (SNs) to a sink node. To this end, we regard an infection as a state transition of a Markov chain and propose a heterogeneous discrete-time Susceptible-Infected-Susceptible (SIS) model to disclose the Diffusion process by combining the SNs heterogeneity with the Malware's spread probability, which is foreseen by a developed non-cooperative non-zero-sum game. We further present reliability and availability measures for a susceptible SN from the perspective of reliability theory, from which we deduce and obtain metrics of reliability and availability assessment of HWSNs with star and cluster topologies. We therefore set up a dependability assessment mechanism for HWSNs with Malware Diffusion. Experiments illustrate the influence of the parameters on Malware's selection of the optimal spread probability and the mean time to infection of a susceptible SN. We also validate the effectiveness of the proposed mechanism. Our results can be applied to set up theoretical bases for governing the employment of reliable techniques.
En Fan - One of the best experts on this subject based on the ideXlab platform.
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Multistage Signaling Game-Based Optimal Detection Strategies for Suppressing Malware Diffusion in Fog-Cloud-Based IoT Networks
IEEE Internet of Things Journal, 2018Co-Authors: Shigen Shen, Haiping Zhou, Longjun Huang, En Fan, Qiying CaoAbstract:We consider the Internet of Things (IoT) with Malware Diffusion and seek optimal Malware detection strategies for preserving the privacy of smart objects in IoT networks and suppressing Malware Diffusion. To this end, we propose a Malware detection infrastructure realized by an intrusion detection system (IDS) with cloud and fog computing to overcome the IDS deployment problem in smart objects due to their limited resources and heterogeneous subnetworks. We then employ a signaling game to disclose interactions between smart objects and the corresponding fog node because of Malware uncertainty in smart objects. To minimize privacy leakage of smart objects, we also develop optimal strategies that maximize Malware detection probability by theoretically computing the perfect Bayesian equilibrium of the game. Moreover, we analyze the factors influencing the optimal probability of a malicious smart object diffusing Malware, and factors influencing the performance of a fog node in determining an infected smart object. Finally, we present a framework to demonstrate a potential and practical application of suppressing Malware Diffusion in IoT networks.
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A non-cooperative non-zero-sum game-based dependability assessment of heterogeneous WSNs with Malware Diffusion
Journal of Network and Computer Applications, 2017Co-Authors: Shigen Shen, Jianhua Liu, En Fan, Qiying CaoAbstract:We consider Heterogeneous Wireless Sensor Networks (HWSNs) with Malware Diffusion and find a solution to assess their dependability in order to guarantee dependable operations on sending sensed data from sensor nodes (SNs) to a sink node. To this end, we regard an infection as a state transition of a Markov chain and propose a heterogeneous discrete-time Susceptible-Infected-Susceptible (SIS) model to disclose the Diffusion process by combining the SNs heterogeneity with the Malware's spread probability, which is foreseen by a developed non-cooperative non-zero-sum game. We further present reliability and availability measures for a susceptible SN from the perspective of reliability theory, from which we deduce and obtain metrics of reliability and availability assessment of HWSNs with star and cluster topologies. We therefore set up a dependability assessment mechanism for HWSNs with Malware Diffusion. Experiments illustrate the influence of the parameters on Malware's selection of the optimal spread probability and the mean time to infection of a susceptible SN. We also validate the effectiveness of the proposed mechanism. Our results can be applied to set up theoretical bases for governing the employment of reliable techniques.
Haiping Zhou - One of the best experts on this subject based on the ideXlab platform.
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HSIRD: A model for characterizing dynamics of Malware Diffusion in heterogeneous WSNs
Journal of Network and Computer Applications, 2019Co-Authors: Shigen Shen, Haiping Zhou, Feng Sheng, Longjun Huang, Jianhua Liu, Qiying CaoAbstract:Abstract Heterogeneous wireless sensor networks (HWSNs), as blocks of the Internet of Things, are vulnerable to Malware Diffusion breaking the data confidentiality and service availability, owing to their weak defense mechanism and poor resilience. Thus, constructing a Malware Diffusion model and revealing the rules of Malware Diffusion in HWSNs are urgently needed. In this context, we propose a Heterogeneous Susceptible-Infectious-Removed-Dead (HSIRD) model based on epidemiology, in order to not only characterize the dead state where a heterogeneous sensor node (HSN) may lose its functionality owing to physical damage or Malware attacks but also represent the HSN communication connectivity, which is one of the heterogeneities that exist universally in HWSNs. We then analyze the dynamics of the fractions of HSNs belonging to different degrees in different states and obtain the corresponding differential equations. Using these equations, we prove the existence of equilibrium points of the HSIRD model. Subsequently, we attain the basic reproduction number governing the stability of the equilibrium points. We further prove the stability of the equilibrium points of the model and attain the conditions indicating whether Malware in HWSNs will diffuse or die out. Finally, we validate the effectiveness of the model via simulation. The results provide a theoretical foundation for suppressing Malware Diffusion in Malware-infected HWSNs.
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Multistage Signaling Game-Based Optimal Detection Strategies for Suppressing Malware Diffusion in Fog-Cloud-Based IoT Networks
IEEE Internet of Things Journal, 2018Co-Authors: Shigen Shen, Haiping Zhou, Longjun Huang, En Fan, Qiying CaoAbstract:We consider the Internet of Things (IoT) with Malware Diffusion and seek optimal Malware detection strategies for preserving the privacy of smart objects in IoT networks and suppressing Malware Diffusion. To this end, we propose a Malware detection infrastructure realized by an intrusion detection system (IDS) with cloud and fog computing to overcome the IDS deployment problem in smart objects due to their limited resources and heterogeneous subnetworks. We then employ a signaling game to disclose interactions between smart objects and the corresponding fog node because of Malware uncertainty in smart objects. To minimize privacy leakage of smart objects, we also develop optimal strategies that maximize Malware detection probability by theoretically computing the perfect Bayesian equilibrium of the game. Moreover, we analyze the factors influencing the optimal probability of a malicious smart object diffusing Malware, and factors influencing the performance of a fog node in determining an infected smart object. Finally, we present a framework to demonstrate a potential and practical application of suppressing Malware Diffusion in IoT networks.