In sediment transport on a mobile erodible bed,near-bed particles tend to preferentially locate under specific flow regions and form an uneven bedform.These moving and mobile particles can significantly modulate turbu...In sediment transport on a mobile erodible bed,near-bed particles tend to preferentially locate under specific flow regions and form an uneven bedform.These moving and mobile particles can significantly modulate turbulence at various scales,from inertial large-scale motions to small viscous motions.This study analyzes the particle-resolved direct numerical simulation data of particle-laden two-phase flow with multilayers of particles in turbulent flows over static and mobile beds.The double-average method is adopted for energy transfer analysis.The result shows that the alternative streaky bedform in the spanwise directions correlates with the streak structures in the near-wall turbulence in the mobile bed case.The energy redistribution and exchange,as well as the dissipation,are analyzed in detail,and an energy transfer diagram is given in the last to summarize the energy transfer processes.In both the static and mobile bed cases,flow energy is introduced into the system via the work performed by volume forces acting on the mean flow.The viscous dissipations in the double mean and form-induced fields are more pronounced in the static bed case,and the work done by the fluid-particle interfacial stress in the double mean and turbulent fields is more pronounced in the mobile bed case.The prominent energy contribution in the form-induced field is the production by the form-induced stress on the mean strain in the mobile bed case.In addition,sediment transport involving a limited number of mobile particles is insufficient to capture the energy transfer processes that occur over the troughs and may intertwine the energy transfer processes over the mobile particles and the fixed particle bed.展开更多
随着5G时代的到来,网络中的用户数量及其访问行为所产生的流量呈爆发式增长。作为互联网基石,域名系统(domain name system,DNS)变得愈加重要,其不仅能够存储海量访问行迹,还能传递活动背后的行为信息。运用大数据技术挖掘DNS的数据信息...随着5G时代的到来,网络中的用户数量及其访问行为所产生的流量呈爆发式增长。作为互联网基石,域名系统(domain name system,DNS)变得愈加重要,其不仅能够存储海量访问行迹,还能传递活动背后的行为信息。运用大数据技术挖掘DNS的数据信息,并对其进行分析处理,从中描述用户的行为逻辑,不仅能为判断未来的流量趋势、促进网络基础建设提供数据支持,还能为信息网络中的通信运营商、互联网企业等提供帮助,促进互联网信息技术的发展。展开更多
With the widespread adoption of encrypted Domain Name System(DNS)technologies such as DNS over Hyper Text Transfer Protocol Secure(HTTPS),traditional port and protocol-based traffic analysis methods have become ineffe...With the widespread adoption of encrypted Domain Name System(DNS)technologies such as DNS over Hyper Text Transfer Protocol Secure(HTTPS),traditional port and protocol-based traffic analysis methods have become ineffective.Although encrypted DNS enhances user privacy protection,it also provides concealed communication channels for malicious software,compelling detection technologies to shift towards statistical featurebased and machine learning approaches.However,these methods still face challenges in real-time performance and privacy protection.This paper proposes a real-time identification technology for encrypted DNS traffic with privacy protection.Firstly,a hierarchical architecture of cloud-edge-end collaboration is designed,incorporating task offloading strategies to balance privacy protection and identification efficiency.Secondly,a privacy-preserving federated learning mechanismbased on Federated Robust Aggregation(FedRA)is proposed,utilizingMedoid aggregation and differential privacy techniques to ensure data privacy and enhance identification accuracy.Finally,an edge offloading strategy based on a dynamic priority scheduling algorithm(DPSA)is designed to alleviate terminal burden and reduce latency.Simulation results demonstrate that the proposed technology significantly improves the accuracy and realtime performance of encrypted DNS traffic identification while protecting privacy,making it suitable for various network environments.展开更多
Domain name system(DNS)tunneling attacks can bypass firewalls,which typically“trust”DNS transmissions by concealing malicious traffic in the packets trusted to convey legitimate ones,thereby making detection using c...Domain name system(DNS)tunneling attacks can bypass firewalls,which typically“trust”DNS transmissions by concealing malicious traffic in the packets trusted to convey legitimate ones,thereby making detection using conventional security techniques challenging.To address this issue,we propose a Lebesgue-2 regularized multilayer perceptron(L2R-MLP)algorithm for detecting DNS tunneling attacks.The DNS dataset was carefully curated from a publicly available repository,and relevant features,such as packet size and count,were selected using the recusive feature elimination technique.L2 regularization in the MLP classifier's hidden layers enhances pattern recognition during training,effectively countering the risk of overfitting.When evaluated against a benchmark MLP model,L2R-MLP demonstrated superior performance with 99.46%accuracy,97.00%precision,97.00%F1-score,99.95%recall,and an AUC of 89.00%.In comparison,the benchmark MLP achieved 92.53%accuracy,96.00%precision,97.00%F1-score,99.95%recall,and an AUC of 87.00%.This highlights the effectiveness of L2 regularization in improving predictive capabilities and model generalization for unseen instances.展开更多
域名系统(Domain Name System,DNS)是互联网中用于将域名解析为IP地址的重要服务,DNS覆盖了绝大多数网络活动场景。关键信息基础设施涉及国家安全和社会稳定,其网络服务的正常运行依赖于DNS解析的完整性。DNS攻击不仅会导致服务中断,还...域名系统(Domain Name System,DNS)是互联网中用于将域名解析为IP地址的重要服务,DNS覆盖了绝大多数网络活动场景。关键信息基础设施涉及国家安全和社会稳定,其网络服务的正常运行依赖于DNS解析的完整性。DNS攻击不仅会导致服务中断,还可能窃取敏感信息,严重威胁关键信息基础设施的安全。因此,研究关键信息基础设施面临的DNS攻击威胁及其防范措施十分必要。文章梳理了DNS攻击的类型及其危害,深入分析了DNS攻击对关键信息基础设施的威胁,并提出了技术与管理协同的纵深防御体系,以期为保障关键信息基础设施的安全稳定运行提供参考。展开更多
域名解析系统(Domain Name System,DNS)是互联网的核心组成部分,负责将域名转化为对应的IP地址。根、顶级域等权威服务器和本地域名服务器(Local DNS,LDNS)共同组成了DNS服务系统,其稳定运行直接关系到全球网络服务的可用性与安全性。基...域名解析系统(Domain Name System,DNS)是互联网的核心组成部分,负责将域名转化为对应的IP地址。根、顶级域等权威服务器和本地域名服务器(Local DNS,LDNS)共同组成了DNS服务系统,其稳定运行直接关系到全球网络服务的可用性与安全性。基于20多年的运维经验,笔者提出构建新型域名解析运营体系,理顺域名所有者、运营者和秩序管理者的责权利关系,以期更好地支撑新型互联网业务的创新发展和网络的稳定运行。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12388101 and 12472221)the Fundamental Research Funds for the Central Universities(Grant No.lzujbky-2024-oy10).
文摘In sediment transport on a mobile erodible bed,near-bed particles tend to preferentially locate under specific flow regions and form an uneven bedform.These moving and mobile particles can significantly modulate turbulence at various scales,from inertial large-scale motions to small viscous motions.This study analyzes the particle-resolved direct numerical simulation data of particle-laden two-phase flow with multilayers of particles in turbulent flows over static and mobile beds.The double-average method is adopted for energy transfer analysis.The result shows that the alternative streaky bedform in the spanwise directions correlates with the streak structures in the near-wall turbulence in the mobile bed case.The energy redistribution and exchange,as well as the dissipation,are analyzed in detail,and an energy transfer diagram is given in the last to summarize the energy transfer processes.In both the static and mobile bed cases,flow energy is introduced into the system via the work performed by volume forces acting on the mean flow.The viscous dissipations in the double mean and form-induced fields are more pronounced in the static bed case,and the work done by the fluid-particle interfacial stress in the double mean and turbulent fields is more pronounced in the mobile bed case.The prominent energy contribution in the form-induced field is the production by the form-induced stress on the mean strain in the mobile bed case.In addition,sediment transport involving a limited number of mobile particles is insufficient to capture the energy transfer processes that occur over the troughs and may intertwine the energy transfer processes over the mobile particles and the fixed particle bed.
文摘随着5G时代的到来,网络中的用户数量及其访问行为所产生的流量呈爆发式增长。作为互联网基石,域名系统(domain name system,DNS)变得愈加重要,其不仅能够存储海量访问行迹,还能传递活动背后的行为信息。运用大数据技术挖掘DNS的数据信息,并对其进行分析处理,从中描述用户的行为逻辑,不仅能为判断未来的流量趋势、促进网络基础建设提供数据支持,还能为信息网络中的通信运营商、互联网企业等提供帮助,促进互联网信息技术的发展。
文摘With the widespread adoption of encrypted Domain Name System(DNS)technologies such as DNS over Hyper Text Transfer Protocol Secure(HTTPS),traditional port and protocol-based traffic analysis methods have become ineffective.Although encrypted DNS enhances user privacy protection,it also provides concealed communication channels for malicious software,compelling detection technologies to shift towards statistical featurebased and machine learning approaches.However,these methods still face challenges in real-time performance and privacy protection.This paper proposes a real-time identification technology for encrypted DNS traffic with privacy protection.Firstly,a hierarchical architecture of cloud-edge-end collaboration is designed,incorporating task offloading strategies to balance privacy protection and identification efficiency.Secondly,a privacy-preserving federated learning mechanismbased on Federated Robust Aggregation(FedRA)is proposed,utilizingMedoid aggregation and differential privacy techniques to ensure data privacy and enhance identification accuracy.Finally,an edge offloading strategy based on a dynamic priority scheduling algorithm(DPSA)is designed to alleviate terminal burden and reduce latency.Simulation results demonstrate that the proposed technology significantly improves the accuracy and realtime performance of encrypted DNS traffic identification while protecting privacy,making it suitable for various network environments.
文摘Domain name system(DNS)tunneling attacks can bypass firewalls,which typically“trust”DNS transmissions by concealing malicious traffic in the packets trusted to convey legitimate ones,thereby making detection using conventional security techniques challenging.To address this issue,we propose a Lebesgue-2 regularized multilayer perceptron(L2R-MLP)algorithm for detecting DNS tunneling attacks.The DNS dataset was carefully curated from a publicly available repository,and relevant features,such as packet size and count,were selected using the recusive feature elimination technique.L2 regularization in the MLP classifier's hidden layers enhances pattern recognition during training,effectively countering the risk of overfitting.When evaluated against a benchmark MLP model,L2R-MLP demonstrated superior performance with 99.46%accuracy,97.00%precision,97.00%F1-score,99.95%recall,and an AUC of 89.00%.In comparison,the benchmark MLP achieved 92.53%accuracy,96.00%precision,97.00%F1-score,99.95%recall,and an AUC of 87.00%.This highlights the effectiveness of L2 regularization in improving predictive capabilities and model generalization for unseen instances.
文摘域名系统(Domain Name System,DNS)是互联网中用于将域名解析为IP地址的重要服务,DNS覆盖了绝大多数网络活动场景。关键信息基础设施涉及国家安全和社会稳定,其网络服务的正常运行依赖于DNS解析的完整性。DNS攻击不仅会导致服务中断,还可能窃取敏感信息,严重威胁关键信息基础设施的安全。因此,研究关键信息基础设施面临的DNS攻击威胁及其防范措施十分必要。文章梳理了DNS攻击的类型及其危害,深入分析了DNS攻击对关键信息基础设施的威胁,并提出了技术与管理协同的纵深防御体系,以期为保障关键信息基础设施的安全稳定运行提供参考。
文摘域名解析系统(Domain Name System,DNS)是互联网的核心组成部分,负责将域名转化为对应的IP地址。根、顶级域等权威服务器和本地域名服务器(Local DNS,LDNS)共同组成了DNS服务系统,其稳定运行直接关系到全球网络服务的可用性与安全性。基于20多年的运维经验,笔者提出构建新型域名解析运营体系,理顺域名所有者、运营者和秩序管理者的责权利关系,以期更好地支撑新型互联网业务的创新发展和网络的稳定运行。
