As cross-chain technologies enable interactions among different blockchains(hereinafter“chains”),multi-chain consensus is becoming increasingly important in blockchain networks.However,more attention has been paid t...As cross-chain technologies enable interactions among different blockchains(hereinafter“chains”),multi-chain consensus is becoming increasingly important in blockchain networks.However,more attention has been paid to single-chain consensus schemes.Multi-chain consensus schemes with trusted miner participation have not been considered,thus offering opportunities for malicious users to launch diverse miner behavior(DMB)attacks on different chains.DMB attackers can be friendly in the consensus process on some chains,called mask chains,to enhance their trust value,while on others,called kill chains,they engage in destructive behaviors on the network.In this paper,we propose a multi-chain consensus scheme named Proof-of-DiscTrust(PoDT)to defend against DMB attacks.The idea of distinctive trust(DiscTrust)is introduced to evaluate the trust value of each user across different chains.The trustworthiness of a user is split into local and global trust values.A dynamic behavior prediction scheme is designed to enforce DiscTrust to prevent an intensive DMB attacker from maintaining strong trust by alternately creating true or false blocks on the kill chain.Three trusted miner selection algorithms for multi-chain environments can be implemented to select network miners,chain miners,and chain miner leaders,separately.Simulation results show that PoDT is secure against DMB attacks and more effective than traditional consensus schemes in multi-chain environments.展开更多
As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UA...As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UAVs. Existing weapon-target assignment methods primarily focus on macro cluster constraints while neglecting individual strategy updates. This paper proposes a novel weapon-target assignment method for UAVs based on the multi-strategy threshold public goods game(PGG). By analyzing the concept mapping between weapon-target assignment for UAVs and multi-strategy threshold PGG, a weapon-target assignment model for UAVs based on the multi-strategy threshold PGG is established, which is adaptively complemented by the diverse cooperation-defection strategy library and the utility function based on the threshold mechanism. Additionally, a multi-chain Markov is formulated to quantitatively describe the stochastic evolutionary dynamics, whose evolutionary stable distribution is theoretically derived through the development of a strategy update rule based on preference-based aspiration dynamic. Numerical simulation results validate the feasibility and effectiveness of the proposed method, and the impacts of selection intensity, preference degree and threshold on the evolutionary stable distribution are analyzed. Comparative simulations show that the proposed method outperforms GWO, DE, and NSGA-II, achieving 17.18% higher expected utility than NSGA-II and reducing evolutionary stable times by 25% in large-scale scenario.展开更多
基金supported by the Natural Science Basic Research Program of Shaanxi Province,China(No.2023-JC-YB-561)。
文摘As cross-chain technologies enable interactions among different blockchains(hereinafter“chains”),multi-chain consensus is becoming increasingly important in blockchain networks.However,more attention has been paid to single-chain consensus schemes.Multi-chain consensus schemes with trusted miner participation have not been considered,thus offering opportunities for malicious users to launch diverse miner behavior(DMB)attacks on different chains.DMB attackers can be friendly in the consensus process on some chains,called mask chains,to enhance their trust value,while on others,called kill chains,they engage in destructive behaviors on the network.In this paper,we propose a multi-chain consensus scheme named Proof-of-DiscTrust(PoDT)to defend against DMB attacks.The idea of distinctive trust(DiscTrust)is introduced to evaluate the trust value of each user across different chains.The trustworthiness of a user is split into local and global trust values.A dynamic behavior prediction scheme is designed to enforce DiscTrust to prevent an intensive DMB attacker from maintaining strong trust by alternately creating true or false blocks on the kill chain.Three trusted miner selection algorithms for multi-chain environments can be implemented to select network miners,chain miners,and chain miner leaders,separately.Simulation results show that PoDT is secure against DMB attacks and more effective than traditional consensus schemes in multi-chain environments.
基金supported by the National Natural Science Foundation of China (No. 62073267)。
文摘As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UAVs. Existing weapon-target assignment methods primarily focus on macro cluster constraints while neglecting individual strategy updates. This paper proposes a novel weapon-target assignment method for UAVs based on the multi-strategy threshold public goods game(PGG). By analyzing the concept mapping between weapon-target assignment for UAVs and multi-strategy threshold PGG, a weapon-target assignment model for UAVs based on the multi-strategy threshold PGG is established, which is adaptively complemented by the diverse cooperation-defection strategy library and the utility function based on the threshold mechanism. Additionally, a multi-chain Markov is formulated to quantitatively describe the stochastic evolutionary dynamics, whose evolutionary stable distribution is theoretically derived through the development of a strategy update rule based on preference-based aspiration dynamic. Numerical simulation results validate the feasibility and effectiveness of the proposed method, and the impacts of selection intensity, preference degree and threshold on the evolutionary stable distribution are analyzed. Comparative simulations show that the proposed method outperforms GWO, DE, and NSGA-II, achieving 17.18% higher expected utility than NSGA-II and reducing evolutionary stable times by 25% in large-scale scenario.
