近年来,使用恶意Excel 4.0宏(XLM)文档的攻击迎来了爆发,而XLM代码往往经过复杂的混淆,现有方法或检测系统难以分析海量样本的真实功能。因此,针对恶意样本中使用的各类混淆技术,基于抽象语法树和模拟执行,设计和实现了包含138个宏函数...近年来,使用恶意Excel 4.0宏(XLM)文档的攻击迎来了爆发,而XLM代码往往经过复杂的混淆,现有方法或检测系统难以分析海量样本的真实功能。因此,针对恶意样本中使用的各类混淆技术,基于抽象语法树和模拟执行,设计和实现了包含138个宏函数处理程序的自动化XLM反混淆与关键威胁指标(IOC,indicators of compromise)提取系统XLMRevealer;在此基础上,根据XLM代码特点提取Word和Token特征,通过特征融合能够捕获多层次细粒度特征,并在XLMRevealer中构造CNN-BiLSTM(convolution neural network-bidirectional long short term memory)模型,从不同维度挖掘家族样本的关联性和完成家族分类。最后,从5个来源构建包含2346个样本的数据集并用于反混淆实验和家族分类实验。实验结果表明,XLMRevealer的反混淆成功率达到71.3%,相比XLMMacroDeobfuscator和SYMBEXCEL工具分别提高了20.8%和15.8%;反混淆效率稳定,平均耗时仅为0.512 s。XLMRevealer对去混淆XLM代码的家族分类准确率高达94.88%,效果优于所有基线模型,有效体现Word和Token特征融合的优势。此外,为探索反混淆对家族分类的影响,并考虑不同家族使用的混淆技术可能有所不同,模型会识别到混淆技术的特征,分别对反混淆前和反混淆后再统一混淆的XLM代码进行实验,家族分类准确率为89.58%、53.61%,证明模型能够学习混淆技术特征,更验证了反混淆对家族分类极大的促进作用。展开更多
Research data infrastructures form the cornerstone in both cyber and physical spaces,driving the progression of the data-intensive scientific research paradigm.This opinion paper presents an overview of global researc...Research data infrastructures form the cornerstone in both cyber and physical spaces,driving the progression of the data-intensive scientific research paradigm.This opinion paper presents an overview of global research data infrastructure,drawing insights from national roadmaps and strategic documents related to research data infrastructure.It emphasizes the pivotal role of research data infrastructures by delineating four new missions aimed at positioning them at the core of the current scientific research and communication ecosystem.The four new missions of research data infrastructures are:(1)as a pioneer,to transcend the disciplinary border and address complex,cutting-edge scientific and social challenges with problem-and data-oriented insights;(2)as an architect,to establish a digital,intelligent,flexible research and knowledge services environment;(3)as a platform,to foster the high-end academic communication;(4)as a coordinator,to balance scientific openness with ethics needs.展开更多
Equal access to social infrastructures is a fundamental prerequisite for sustainable development,but has long been a great challenge worldwide.Previous studies have primarily focused on the accessibility to social inf...Equal access to social infrastructures is a fundamental prerequisite for sustainable development,but has long been a great challenge worldwide.Previous studies have primarily focused on the accessibility to social infras-tructures in urban areas across various scales,with less attention to rural areas,where inequality can be more severe.Particularly,few have investigated the disparities of accessibility to social infrastructures between urban and rural areas.Here,using the Changsha-Zhuzhou-Xiangtan urban agglomeration,China,as an example,we investigated the inequality of accessibility in both urban and rural areas,and further compared the urban-rural difference.Accessibility was measured by travel time of residents to infrastructures.We selected four types of social infrastructures including supermarkets,bus stops,primary schools,and health care,which were funda-mentally important to both urban and rural residents.We found large disparities in accessibility between urban and rural areas,ranging from 20 min to 2 h.Rural residents had to spend one to two more hours to bus stops than urban residents,and 20 min more to the other three types of infrastructures.Furthermore,accessibility to multiple infrastructures showed greater urban-rural differences.Rural residents in more than half of the towns had no access to any infrastructure within 15 min,while more than 60%of the urban residents could access to all infrastructures within 15 min.Our results revealed quantitative accessibility gap between urban and rural areas and underscored the necessity of social infrastructures planning to address such disparities.展开更多
Due to the need for massive device connectivity,low communication latency,and various customizations in 6G architecture,a distributed cloud deployment approach will be more relevant to the space-air-ground-sea integra...Due to the need for massive device connectivity,low communication latency,and various customizations in 6G architecture,a distributed cloud deployment approach will be more relevant to the space-air-ground-sea integrated network scenario.However,the openness and heterogeneity of the 6G network cause the problems of network security.To improve the trustworthiness of 6G networks,we propose a trusted computing-based approach for establishing trust relationships inmulti-cloud scenarios.The proposed method shows the relationship of trust based on dual-level verification.It separates the trustworthy states of multiple complex cloud units in 6G architecture into the state within and between cloud units.Firstly,SM3 algorithm establishes the chain of trust for the system’s trusted boot phase.Then,the remote attestation server(RAS)of distributed cloud units verifies the physical servers.Meanwhile,the physical servers use a ring approach to verify the cloud servers.Eventually,the centralized RAS takes one-time authentication to the critical evidence information of distributed cloud unit servers.Simultaneously,the centralized RAS also verifies the evidence of distributed RAS.We establish our proposed approach in a natural OpenStack-based cloud environment.The simulation results show that the proposed method achieves higher security with less than a 1%system performance loss.展开更多
文摘近年来,使用恶意Excel 4.0宏(XLM)文档的攻击迎来了爆发,而XLM代码往往经过复杂的混淆,现有方法或检测系统难以分析海量样本的真实功能。因此,针对恶意样本中使用的各类混淆技术,基于抽象语法树和模拟执行,设计和实现了包含138个宏函数处理程序的自动化XLM反混淆与关键威胁指标(IOC,indicators of compromise)提取系统XLMRevealer;在此基础上,根据XLM代码特点提取Word和Token特征,通过特征融合能够捕获多层次细粒度特征,并在XLMRevealer中构造CNN-BiLSTM(convolution neural network-bidirectional long short term memory)模型,从不同维度挖掘家族样本的关联性和完成家族分类。最后,从5个来源构建包含2346个样本的数据集并用于反混淆实验和家族分类实验。实验结果表明,XLMRevealer的反混淆成功率达到71.3%,相比XLMMacroDeobfuscator和SYMBEXCEL工具分别提高了20.8%和15.8%;反混淆效率稳定,平均耗时仅为0.512 s。XLMRevealer对去混淆XLM代码的家族分类准确率高达94.88%,效果优于所有基线模型,有效体现Word和Token特征融合的优势。此外,为探索反混淆对家族分类的影响,并考虑不同家族使用的混淆技术可能有所不同,模型会识别到混淆技术的特征,分别对反混淆前和反混淆后再统一混淆的XLM代码进行实验,家族分类准确率为89.58%、53.61%,证明模型能够学习混淆技术特征,更验证了反混淆对家族分类极大的促进作用。
基金the National Social Science Fund of China(Grant No.22CTQ031)Special Project on Library Capacity Building of the Chinese Academy of Sciences(Grant No.E2290431).
文摘Research data infrastructures form the cornerstone in both cyber and physical spaces,driving the progression of the data-intensive scientific research paradigm.This opinion paper presents an overview of global research data infrastructure,drawing insights from national roadmaps and strategic documents related to research data infrastructure.It emphasizes the pivotal role of research data infrastructures by delineating four new missions aimed at positioning them at the core of the current scientific research and communication ecosystem.The four new missions of research data infrastructures are:(1)as a pioneer,to transcend the disciplinary border and address complex,cutting-edge scientific and social challenges with problem-and data-oriented insights;(2)as an architect,to establish a digital,intelligent,flexible research and knowledge services environment;(3)as a platform,to foster the high-end academic communication;(4)as a coordinator,to balance scientific openness with ethics needs.
基金supported by funding from the National Natural Science Foundation of China(Grant No.U21A2010)the National Science Fund for Distinguished Young Scholars(Grant No.42225104)the National Key Research and Development Program(Grant No.2022YFF130110O).
文摘Equal access to social infrastructures is a fundamental prerequisite for sustainable development,but has long been a great challenge worldwide.Previous studies have primarily focused on the accessibility to social infras-tructures in urban areas across various scales,with less attention to rural areas,where inequality can be more severe.Particularly,few have investigated the disparities of accessibility to social infrastructures between urban and rural areas.Here,using the Changsha-Zhuzhou-Xiangtan urban agglomeration,China,as an example,we investigated the inequality of accessibility in both urban and rural areas,and further compared the urban-rural difference.Accessibility was measured by travel time of residents to infrastructures.We selected four types of social infrastructures including supermarkets,bus stops,primary schools,and health care,which were funda-mentally important to both urban and rural residents.We found large disparities in accessibility between urban and rural areas,ranging from 20 min to 2 h.Rural residents had to spend one to two more hours to bus stops than urban residents,and 20 min more to the other three types of infrastructures.Furthermore,accessibility to multiple infrastructures showed greater urban-rural differences.Rural residents in more than half of the towns had no access to any infrastructure within 15 min,while more than 60%of the urban residents could access to all infrastructures within 15 min.Our results revealed quantitative accessibility gap between urban and rural areas and underscored the necessity of social infrastructures planning to address such disparities.
基金This work was supported by the Ministry of Education and China Mobile Research Fund Project(MCM20200102)the 173 Project(No.2019-JCJQ-ZD-342-00)+2 种基金the National Natural Science Foundation of China(No.U19A2081)the Fundamental Research Funds for the Central Universities(No.2023SCU12129)the Science and Engineering Connotation Development Project of Sichuan University(No.2020SCUNG129).
文摘Due to the need for massive device connectivity,low communication latency,and various customizations in 6G architecture,a distributed cloud deployment approach will be more relevant to the space-air-ground-sea integrated network scenario.However,the openness and heterogeneity of the 6G network cause the problems of network security.To improve the trustworthiness of 6G networks,we propose a trusted computing-based approach for establishing trust relationships inmulti-cloud scenarios.The proposed method shows the relationship of trust based on dual-level verification.It separates the trustworthy states of multiple complex cloud units in 6G architecture into the state within and between cloud units.Firstly,SM3 algorithm establishes the chain of trust for the system’s trusted boot phase.Then,the remote attestation server(RAS)of distributed cloud units verifies the physical servers.Meanwhile,the physical servers use a ring approach to verify the cloud servers.Eventually,the centralized RAS takes one-time authentication to the critical evidence information of distributed cloud unit servers.Simultaneously,the centralized RAS also verifies the evidence of distributed RAS.We establish our proposed approach in a natural OpenStack-based cloud environment.The simulation results show that the proposed method achieves higher security with less than a 1%system performance loss.
