新一代信息技术与工业系统深度融合,提升了工业控制系统和工业设备网络的连接性,使得工业互联网成为APT攻击的重点目标.针对现有偏向于静态认证的方法难以识别APT攻击者控制内部失陷终端获取的“傀儡身份”,进而造成敏感数据泄露的问题...新一代信息技术与工业系统深度融合,提升了工业控制系统和工业设备网络的连接性,使得工业互联网成为APT攻击的重点目标.针对现有偏向于静态认证的方法难以识别APT攻击者控制内部失陷终端获取的“傀儡身份”,进而造成敏感数据泄露的问题,提出一种面向工业互联网的零信任动态认证方案.融合CNN-BiLSTM构建混合神经网络,利用其时序特性设计行为因子预测模型.通过多个残差块组成的深度卷积网络提取特征,双向长短时记忆网络(bidirectional long short-term memory,BiLSTM)进行时间序列分析,生成对主体的行为因子预测,作为零信任动态认证重要凭据.为快速识别“傀儡身份”,融入行为因子设计IPK-SPA动态认证机制.利用轻量级标识公钥技术适应工业互联网海量末梢,借助零信任单包授权技术隐藏工控网络边界.安全性分析和实验结果表明,提出的动态认证方案具有较好的“傀儡身份”识别能力,有助于抗击工业互联网环境下因APT攻击者窃取身份导致的数据窃密威胁.展开更多
The increase in number of people using the Internet leads to increased cyberattack opportunities.Advanced Persistent Threats,or APTs,are among the most dangerous targeted cyberattacks.APT attacks utilize various advan...The increase in number of people using the Internet leads to increased cyberattack opportunities.Advanced Persistent Threats,or APTs,are among the most dangerous targeted cyberattacks.APT attacks utilize various advanced tools and techniques for attacking targets with specific goals.Even countries with advanced technologies,like the US,Russia,the UK,and India,are susceptible to this targeted attack.APT is a sophisticated attack that involves multiple stages and specific strategies.Besides,TTP(Tools,Techniques,and Procedures)involved in the APT attack are commonly new and developed by an attacker to evade the security system.However,APTs are generally implemented in multiple stages.If one of the stages is detected,we may apply a defense mechanism for subsequent stages,leading to the entire APT attack failure.The detection at the early stage of APT and the prediction of the next step in the APT kill chain are ongoing challenges.This survey paper will provide knowledge about APT attacks and their essential steps.This follows the case study of known APT attacks,which will give clear information about the APT attack process—in later sections,highlighting the various detection methods defined by different researchers along with the limitations of the work.Data used in this article comes from the various annual reports published by security experts and blogs and information released by the enterprise networks targeted by the attack.展开更多
Due to the rapid evolution of Advanced Persistent Threats(APTs)attacks,the emergence of new and rare attack samples,and even those never seen before,make it challenging for traditional rule-based detection methods to ...Due to the rapid evolution of Advanced Persistent Threats(APTs)attacks,the emergence of new and rare attack samples,and even those never seen before,make it challenging for traditional rule-based detection methods to extract universal rules for effective detection.With the progress in techniques such as transfer learning and meta-learning,few-shot network attack detection has progressed.However,challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning,difficulties in capturing rich information from original flow in the case of insufficient samples,and the challenge of high-level abstract representation.To address these challenges,a few-shot network attack detection based on NFHP(Network Flow Holographic Picture)-RN(ResNet)is proposed.Specifically,leveraging inherent properties of images such as translation invariance,rotation invariance,scale invariance,and illumination invariance,network attack traffic features and contextual relationships are intuitively represented in NFHP.In addition,an improved RN network model is employed for high-level abstract feature extraction,ensuring that the extracted high-level abstract features maintain the detailed characteristics of the original traffic behavior,regardless of changes in background traffic.Finally,a meta-learning model based on the self-attention mechanism is constructed,achieving the detection of novel APT few-shot network attacks through the empirical generalization of high-level abstract feature representations of known-class network attack behaviors.Experimental results demonstrate that the proposed method can learn high-level abstract features of network attacks across different traffic detail granularities.Comparedwith state-of-the-artmethods,it achieves favorable accuracy,precision,recall,and F1 scores for the identification of unknown-class network attacks through cross-validation onmultiple datasets.展开更多
The long-period-stacking-ordered(LPSO)structure affects the mechanical,corrosion and hydrolysis properties of Mg alloys.The current work employs high angle annular dark field-scanning transmission electron microscopy(...The long-period-stacking-ordered(LPSO)structure affects the mechanical,corrosion and hydrolysis properties of Mg alloys.The current work employs high angle annular dark field-scanning transmission electron microscopy(HAADF-STEM)and atom probe tomography(APT)to investigate the structural and local chemical information of LPSO phases formed in Mg-Ni-Y/Sm ternary alloys after extended isothermal annealing.Depending on the alloying elements and their concentrations,Mg-Ni-Y/Sm develops a two-phase LPSO+α-Mg structure in which the LPSO phase contains defects,hybrid LPSO structure,and Mg insertions.HAADF-STEM and APT indicate non-stoichiometric LPSO with incomplete Ni_(6)(Y/Sm)_(8) clusters.In addition,the APT quantitatively determines the local composition of LPSO and confirms the presence of Ni within the Mg bonding layers.These results provide insight into a better understanding of the structure and hydrolysis properties of LPSO-Mg alloys.展开更多
文摘新一代信息技术与工业系统深度融合,提升了工业控制系统和工业设备网络的连接性,使得工业互联网成为APT攻击的重点目标.针对现有偏向于静态认证的方法难以识别APT攻击者控制内部失陷终端获取的“傀儡身份”,进而造成敏感数据泄露的问题,提出一种面向工业互联网的零信任动态认证方案.融合CNN-BiLSTM构建混合神经网络,利用其时序特性设计行为因子预测模型.通过多个残差块组成的深度卷积网络提取特征,双向长短时记忆网络(bidirectional long short-term memory,BiLSTM)进行时间序列分析,生成对主体的行为因子预测,作为零信任动态认证重要凭据.为快速识别“傀儡身份”,融入行为因子设计IPK-SPA动态认证机制.利用轻量级标识公钥技术适应工业互联网海量末梢,借助零信任单包授权技术隐藏工控网络边界.安全性分析和实验结果表明,提出的动态认证方案具有较好的“傀儡身份”识别能力,有助于抗击工业互联网环境下因APT攻击者窃取身份导致的数据窃密威胁.
文摘The increase in number of people using the Internet leads to increased cyberattack opportunities.Advanced Persistent Threats,or APTs,are among the most dangerous targeted cyberattacks.APT attacks utilize various advanced tools and techniques for attacking targets with specific goals.Even countries with advanced technologies,like the US,Russia,the UK,and India,are susceptible to this targeted attack.APT is a sophisticated attack that involves multiple stages and specific strategies.Besides,TTP(Tools,Techniques,and Procedures)involved in the APT attack are commonly new and developed by an attacker to evade the security system.However,APTs are generally implemented in multiple stages.If one of the stages is detected,we may apply a defense mechanism for subsequent stages,leading to the entire APT attack failure.The detection at the early stage of APT and the prediction of the next step in the APT kill chain are ongoing challenges.This survey paper will provide knowledge about APT attacks and their essential steps.This follows the case study of known APT attacks,which will give clear information about the APT attack process—in later sections,highlighting the various detection methods defined by different researchers along with the limitations of the work.Data used in this article comes from the various annual reports published by security experts and blogs and information released by the enterprise networks targeted by the attack.
基金supported by the National Natural Science Foundation of China(Nos.U19A208162202320)+2 种基金the Fundamental Research Funds for the Central Universities(No.SCU2023D008)the Science and Engineering Connotation Development Project of Sichuan University(No.2020SCUNG129)the Key Laboratory of Data Protection and Intelligent Management(Sichuan University),Ministry of Education.
文摘Due to the rapid evolution of Advanced Persistent Threats(APTs)attacks,the emergence of new and rare attack samples,and even those never seen before,make it challenging for traditional rule-based detection methods to extract universal rules for effective detection.With the progress in techniques such as transfer learning and meta-learning,few-shot network attack detection has progressed.However,challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning,difficulties in capturing rich information from original flow in the case of insufficient samples,and the challenge of high-level abstract representation.To address these challenges,a few-shot network attack detection based on NFHP(Network Flow Holographic Picture)-RN(ResNet)is proposed.Specifically,leveraging inherent properties of images such as translation invariance,rotation invariance,scale invariance,and illumination invariance,network attack traffic features and contextual relationships are intuitively represented in NFHP.In addition,an improved RN network model is employed for high-level abstract feature extraction,ensuring that the extracted high-level abstract features maintain the detailed characteristics of the original traffic behavior,regardless of changes in background traffic.Finally,a meta-learning model based on the self-attention mechanism is constructed,achieving the detection of novel APT few-shot network attacks through the empirical generalization of high-level abstract feature representations of known-class network attack behaviors.Experimental results demonstrate that the proposed method can learn high-level abstract features of network attacks across different traffic detail granularities.Comparedwith state-of-the-artmethods,it achieves favorable accuracy,precision,recall,and F1 scores for the identification of unknown-class network attacks through cross-validation onmultiple datasets.
基金the financial support provided by ANR(Project ANR-22-PEHY-0007)DGA(French Direction Générale des Armées,2018600045)Région Nouvelle Aquitaine(agreement 2018–1R10126).
文摘The long-period-stacking-ordered(LPSO)structure affects the mechanical,corrosion and hydrolysis properties of Mg alloys.The current work employs high angle annular dark field-scanning transmission electron microscopy(HAADF-STEM)and atom probe tomography(APT)to investigate the structural and local chemical information of LPSO phases formed in Mg-Ni-Y/Sm ternary alloys after extended isothermal annealing.Depending on the alloying elements and their concentrations,Mg-Ni-Y/Sm develops a two-phase LPSO+α-Mg structure in which the LPSO phase contains defects,hybrid LPSO structure,and Mg insertions.HAADF-STEM and APT indicate non-stoichiometric LPSO with incomplete Ni_(6)(Y/Sm)_(8) clusters.In addition,the APT quantitatively determines the local composition of LPSO and confirms the presence of Ni within the Mg bonding layers.These results provide insight into a better understanding of the structure and hydrolysis properties of LPSO-Mg alloys.
