Poly (EA-MAn-APTES)/silica hybrid materials were successfully prepared fromEthyl acrylate (EA), maleic anhydride (MAn) and tetraethoxysilane (TEOS) in the presence of acoupling agent 3-aminopropyltriethoxysilane (APTE...Poly (EA-MAn-APTES)/silica hybrid materials were successfully prepared fromEthyl acrylate (EA), maleic anhydride (MAn) and tetraethoxysilane (TEOS) in the presence of acoupling agent 3-aminopropyltriethoxysilane (APTES),by free-radical solution polymerization and insitu sol-gel process. The mass fraction of TEOS varied from 0 to 25%. The hybrid materials werecharacterized by the methods of FT-IR spectra, solvent extraction, scanning electron microscope (SEM), transmission electron microscope (TEM), differential scanning calorimetry (DSC) andthermogravimetric analysis (TGA) measuring apparatus to get their structures, gel contents,morphologies, particle sizes and thermal performances. The results show that the covalent bonds arebetween organic and inorganic phases, gel contents in the hybrid materials are much higher, theSiO_2 phase is well dispersed in the polymer matrix, silicon dioxide exist at nanoscale in thecomposites and have excellent thermal stability.展开更多
Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address thes...Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address these challenges,we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework(UGEA-LMD).First,the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution,enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem.Second,in the embedding space,we model the dependency structure among feature dimensions using a Gaussian copula to quantify the uncertainty distribution,and generate augmented samples with consistent structural and semantic properties through adaptive sampling,thus expanding the representation space of sparse samples and enhancing the model’s generalization under sparse sample conditions.Unlike static graph methods that cannot model temporal dependencies or data augmentation techniques that depend on predefined structures,UGEA-LMD offers both superior temporaldynamic modeling and structural generalization.Experimental results on the large-scale LANL log dataset demonstrate that,under the transductive setting,UGEA-LMD achieves an AUC of 0.9254;even when 10%of nodes or edges are withheld during training,UGEA-LMD significantly outperforms baseline methods on metrics such as recall and AUC,confirming its robustness and generalization capability in sparse-sample and cold-start scenarios.展开更多
文摘Poly (EA-MAn-APTES)/silica hybrid materials were successfully prepared fromEthyl acrylate (EA), maleic anhydride (MAn) and tetraethoxysilane (TEOS) in the presence of acoupling agent 3-aminopropyltriethoxysilane (APTES),by free-radical solution polymerization and insitu sol-gel process. The mass fraction of TEOS varied from 0 to 25%. The hybrid materials werecharacterized by the methods of FT-IR spectra, solvent extraction, scanning electron microscope (SEM), transmission electron microscope (TEM), differential scanning calorimetry (DSC) andthermogravimetric analysis (TGA) measuring apparatus to get their structures, gel contents,morphologies, particle sizes and thermal performances. The results show that the covalent bonds arebetween organic and inorganic phases, gel contents in the hybrid materials are much higher, theSiO_2 phase is well dispersed in the polymer matrix, silicon dioxide exist at nanoscale in thecomposites and have excellent thermal stability.
基金supported by the Zhongyuan University of Technology Discipline Backbone Teacher Support Program Project(No.GG202417)the Key Research and Development Program of Henan under Grant 251111212000.
文摘Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address these challenges,we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework(UGEA-LMD).First,the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution,enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem.Second,in the embedding space,we model the dependency structure among feature dimensions using a Gaussian copula to quantify the uncertainty distribution,and generate augmented samples with consistent structural and semantic properties through adaptive sampling,thus expanding the representation space of sparse samples and enhancing the model’s generalization under sparse sample conditions.Unlike static graph methods that cannot model temporal dependencies or data augmentation techniques that depend on predefined structures,UGEA-LMD offers both superior temporaldynamic modeling and structural generalization.Experimental results on the large-scale LANL log dataset demonstrate that,under the transductive setting,UGEA-LMD achieves an AUC of 0.9254;even when 10%of nodes or edges are withheld during training,UGEA-LMD significantly outperforms baseline methods on metrics such as recall and AUC,confirming its robustness and generalization capability in sparse-sample and cold-start scenarios.
