With the rapid development of global information and the increasing dependence on network for people, network security problems are becoming more and more serious. By analyzing the existing security assessment methods...With the rapid development of global information and the increasing dependence on network for people, network security problems are becoming more and more serious. By analyzing the existing security assessment methods, we propose a network security situation evaluation system based on modified D-S evidence theory is proposed. Firstly, we give a modified D-S evidence theory to improve the reliability and rationality of the fusion result and apply the theory to correlation analysis. Secondly, the attack successful support is accurately calculated by matching internal factors with external threats. Multi-module evaluation is established to comprehensively evaluate the situation of network security. Finally we use an example of actual network datasets to validate the network security situation evaluation system. The simulation result shows that the system can not only reduce the rate of false positives and false alarms, but also effectively help analysts comprehensively to understand the situation of network security.展开更多
Multimodal perception is a foundational technology for human perception in complex environments.These environments often involve various interference conditions and sensor technical limitations that constrain the info...Multimodal perception is a foundational technology for human perception in complex environments.These environments often involve various interference conditions and sensor technical limitations that constrain the information capture capabilities of single-modality sensors.Multimodal perception addresses these by integrating complementary multisource heterogeneous information,providing a solution for perceiving complex environments.This technology spans across fields such as autonomous driving,industrial detection,biomedical engineering,and remote sensing.However,challenges arise due to multisensor misalignment,inadequate appearance forms,and perception-oriented issues,which complicate the corresponding relationship,information representation,and task-driven fusion.In this context,the advancement of artificial intelligence(AI)has driven the development of information fusion,offering a new perspective on tackling these challenges.1 AI leverages deep neural networks(DNNs)with gradient descent optimization to learn statistical regularities from multimodal data.By examining the entire process of multimodal information fusion,we can gain deeper insights into AI’s working mechanisms and enhance our understanding of AI perception in complex environments.展开更多
基金Supported by the Foundation of Tianjin for Science and Technology Innovation(10FDZDGX00400,11ZCKFGX00900)Key Project of Educational Reform Foundation of Tianjin Municipal Education Commission(C03-0809)
文摘With the rapid development of global information and the increasing dependence on network for people, network security problems are becoming more and more serious. By analyzing the existing security assessment methods, we propose a network security situation evaluation system based on modified D-S evidence theory is proposed. Firstly, we give a modified D-S evidence theory to improve the reliability and rationality of the fusion result and apply the theory to correlation analysis. Secondly, the attack successful support is accurately calculated by matching internal factors with external threats. Multi-module evaluation is established to comprehensively evaluate the situation of network security. Finally we use an example of actual network datasets to validate the network security situation evaluation system. The simulation result shows that the system can not only reduce the rate of false positives and false alarms, but also effectively help analysts comprehensively to understand the situation of network security.
文摘Multimodal perception is a foundational technology for human perception in complex environments.These environments often involve various interference conditions and sensor technical limitations that constrain the information capture capabilities of single-modality sensors.Multimodal perception addresses these by integrating complementary multisource heterogeneous information,providing a solution for perceiving complex environments.This technology spans across fields such as autonomous driving,industrial detection,biomedical engineering,and remote sensing.However,challenges arise due to multisensor misalignment,inadequate appearance forms,and perception-oriented issues,which complicate the corresponding relationship,information representation,and task-driven fusion.In this context,the advancement of artificial intelligence(AI)has driven the development of information fusion,offering a new perspective on tackling these challenges.1 AI leverages deep neural networks(DNNs)with gradient descent optimization to learn statistical regularities from multimodal data.By examining the entire process of multimodal information fusion,we can gain deeper insights into AI’s working mechanisms and enhance our understanding of AI perception in complex environments.
