In distributed fusion,when one or more sensors are disturbed by faults,a common problem is that their local estimations are inconsistent with those of other fault-free sensors.Most of the existing fault-tolerant distr...In distributed fusion,when one or more sensors are disturbed by faults,a common problem is that their local estimations are inconsistent with those of other fault-free sensors.Most of the existing fault-tolerant distributed fusion algorithms,such as the Covariance Union(CU)and Faulttolerant Generalized Convex Combination(FGCC),are only used for the point estimation case where local estimates and their associated error covariances are provided.A treatment with focus on the fault-tolerant distributed fusions of arbitrary local Probability Density Functions(PDFs)is lacking.For this problem,we first propose Kullback–Leibler Divergence(KLD)and reversed KLD induced functional Fuzzy c-Means(FCM)clustering algorithms to soft cluster all local PDFs,respectively.On this basis,two fault-tolerant distributed fusion algorithms of arbitrary local PDFs are then developed.They select the representing PDF of the cluster with the largest sum of memberships as the fused PDF.Numerical examples verify the better fault tolerance of the developed two distributed fusion algorithms.展开更多
The growing use of Portable Document Format(PDF)files across various sectors such as education,government,and business has inadvertently turned them into a major target for cyberattacks.Cybercriminals take advantage o...The growing use of Portable Document Format(PDF)files across various sectors such as education,government,and business has inadvertently turned them into a major target for cyberattacks.Cybercriminals take advantage of the inherent flexibility and layered structure ofPDFs to inject malicious content,often employing advanced obfuscation techniques to evade detection by traditional signature-based security systems.These conventional methods are no longer adequate,especially against sophisticated threats like zero-day exploits and polymorphic malware.In response to these challenges,this study introduces a machine learning-based detection framework specifically designed to combat such threats.Central to the proposed solution is a stacked ensemble learning model that combines the strengths of four high-performing classifiers:Random Forest(RF),Extreme Gradient Boosting(XGB),LightGBM(LGBM),and CatBoost(CB).These models operate in parallel as base learners,each capturing different aspects of the data.Their outputs are then refined by a Gradient Boosting Classifier(GBC),which serves as a meta-learner to enhance prediction accuracy.To ensure the model remains both efficient and effective,Principal Component Analysis(PCA)is applied to reduce feature dimensionality while preserving critical information necessary for malware classification.The model is trained and validated using the CIC-Evasive PDFMalware2022 dataset,which includes a wide range of both malicious and benign PDF samples.The results demonstrate that the framework achieves impressive performance,with 97.10% accuracy and a 97.39% F1-score,surpassing several existing techniques.To enhance trust and interpretability,the system incorporates Local Interpretable Model-agnostic Explanations(LIME),which provides user-friendly insights into the rationale behind each prediction.This research emphasizes how the integration of ensemble learning,feature reduction,and explainable AI can lead to a practical and scalable solution for detecting complex PDF-based threats.The proposed framework lays the foundation for the next generation of intelligent,resilient cybersecurity systems that can address ever-evolving attack strategies.展开更多
Seismic amplitude variation with offset(AVO)inversion is a cornerstone of oil and gas reservoir prediction,enabling the estimation of subsurface elastic parameters and characterization of stratigraphic interfaces.Howe...Seismic amplitude variation with offset(AVO)inversion is a cornerstone of oil and gas reservoir prediction,enabling the estimation of subsurface elastic parameters and characterization of stratigraphic interfaces.However,balancing inversion accuracy and computational efficiency remains a critical challenge.To address this,we propose a novel probabilistic AVO inversion framework integrating three key innovations.First,we derive a high-precision quadratic approximation for compressional(P-wave)reflectivity by retaining first-and second-order terms from the exact Zoeppritz equations through a perturbation strategy.This approach significantly enhances accuracy compared to conventional linear approximations,particularly in reflecting the true amplitude variation at large angles.Subsequently,to improve lateral continuity and stratigraphic resolution,we introduce an instantaneous phase constraint derived via the Hilbert transform.This constraint leverages phase sensitivity to seismic waveform coherence,ensuring geologically consistent interface characterization during stochastic inversion.Furthermore,we develop a hybrid Markov Chain Monte Carlo(MCMC)algorithm combining adaptive Gibbs sampling with the independent doubly adaptive rejection Metropolis sampling(IA2RMS)method.This framework efficiently samples high-dimensional posterior probability density functions(PDFs)of elastic pa rameters:Gibbs sampling gene rates adaptive proposal distributions,while IA2RMS accele rates Markov chain convergence through location-and scale-adjustable proposals.Numerical experiments and field seismic data demonstrate the robustness and feasibility of the proposed probabilistic AVO inversion method.展开更多
基金supported in part by the Open Fund of Intelligent Control Laboratory,China(No.ICL-2023–0202)in part by National Key R&D Program of China(Nos.2021YFC2202600,2021YFC2202603)。
摘要In distributed fusion,when one or more sensors are disturbed by faults,a common problem is that their local estimations are inconsistent with those of other fault-free sensors.Most of the existing fault-tolerant distributed fusion algorithms,such as the Covariance Union(CU)and Faulttolerant Generalized Convex Combination(FGCC),are only used for the point estimation case where local estimates and their associated error covariances are provided.A treatment with focus on the fault-tolerant distributed fusions of arbitrary local Probability Density Functions(PDFs)is lacking.For this problem,we first propose Kullback–Leibler Divergence(KLD)and reversed KLD induced functional Fuzzy c-Means(FCM)clustering algorithms to soft cluster all local PDFs,respectively.On this basis,two fault-tolerant distributed fusion algorithms of arbitrary local PDFs are then developed.They select the representing PDF of the cluster with the largest sum of memberships as the fused PDF.Numerical examples verify the better fault tolerance of the developed two distributed fusion algorithms.
摘要The growing use of Portable Document Format(PDF)files across various sectors such as education,government,and business has inadvertently turned them into a major target for cyberattacks.Cybercriminals take advantage of the inherent flexibility and layered structure ofPDFs to inject malicious content,often employing advanced obfuscation techniques to evade detection by traditional signature-based security systems.These conventional methods are no longer adequate,especially against sophisticated threats like zero-day exploits and polymorphic malware.In response to these challenges,this study introduces a machine learning-based detection framework specifically designed to combat such threats.Central to the proposed solution is a stacked ensemble learning model that combines the strengths of four high-performing classifiers:Random Forest(RF),Extreme Gradient Boosting(XGB),LightGBM(LGBM),and CatBoost(CB).These models operate in parallel as base learners,each capturing different aspects of the data.Their outputs are then refined by a Gradient Boosting Classifier(GBC),which serves as a meta-learner to enhance prediction accuracy.To ensure the model remains both efficient and effective,Principal Component Analysis(PCA)is applied to reduce feature dimensionality while preserving critical information necessary for malware classification.The model is trained and validated using the CIC-Evasive PDFMalware2022 dataset,which includes a wide range of both malicious and benign PDF samples.The results demonstrate that the framework achieves impressive performance,with 97.10% accuracy and a 97.39% F1-score,surpassing several existing techniques.To enhance trust and interpretability,the system incorporates Local Interpretable Model-agnostic Explanations(LIME),which provides user-friendly insights into the rationale behind each prediction.This research emphasizes how the integration of ensemble learning,feature reduction,and explainable AI can lead to a practical and scalable solution for detecting complex PDF-based threats.The proposed framework lays the foundation for the next generation of intelligent,resilient cybersecurity systems that can address ever-evolving attack strategies.
基金supported in part by the Fund of State Key Laboratory of Deep Oil and Gas,China University of Petroleum(East China)under Grant SKLDOG2024-ZYTS-02in part by the National Natural Science Foundation of China under Grant42274157+1 种基金in part by the Fundamental Research Funds for the Central Universities under Grant 24CX07004Ain part by the CNPC Innovation Fund under Grant 2024DQ02-0505。
摘要Seismic amplitude variation with offset(AVO)inversion is a cornerstone of oil and gas reservoir prediction,enabling the estimation of subsurface elastic parameters and characterization of stratigraphic interfaces.However,balancing inversion accuracy and computational efficiency remains a critical challenge.To address this,we propose a novel probabilistic AVO inversion framework integrating three key innovations.First,we derive a high-precision quadratic approximation for compressional(P-wave)reflectivity by retaining first-and second-order terms from the exact Zoeppritz equations through a perturbation strategy.This approach significantly enhances accuracy compared to conventional linear approximations,particularly in reflecting the true amplitude variation at large angles.Subsequently,to improve lateral continuity and stratigraphic resolution,we introduce an instantaneous phase constraint derived via the Hilbert transform.This constraint leverages phase sensitivity to seismic waveform coherence,ensuring geologically consistent interface characterization during stochastic inversion.Furthermore,we develop a hybrid Markov Chain Monte Carlo(MCMC)algorithm combining adaptive Gibbs sampling with the independent doubly adaptive rejection Metropolis sampling(IA2RMS)method.This framework efficiently samples high-dimensional posterior probability density functions(PDFs)of elastic pa rameters:Gibbs sampling gene rates adaptive proposal distributions,while IA2RMS accele rates Markov chain convergence through location-and scale-adjustable proposals.Numerical experiments and field seismic data demonstrate the robustness and feasibility of the proposed probabilistic AVO inversion method.