Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive data.Traditional detection methods often fail to keep pace with ...Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive data.Traditional detection methods often fail to keep pace with the evolving sophistication of cyber threats.This paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack detection.Our approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in performance.It demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant loads.Furthermore,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time applications.Designed for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current defenses.This innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.展开更多
Triaxial testing serves as a fundamental method for evaluating the elastic and strength properties of rocks,crucial for developing accurate 3D geomechanical models.This paper presents a novel method for determining st...Triaxial testing serves as a fundamental method for evaluating the elastic and strength properties of rocks,crucial for developing accurate 3D geomechanical models.This paper presents a novel method for determining strength parameters by incorporating the dependence of uniaxial compressive strength(UCS)on P-wave velocity into the Hoek-Brown criterion.Additionally,a new approach is introduced to process triaxial test data efficiently using Python libraries such as SciPy,NumPy,Matplotlib,and Pandas.Furthermore,the paper addresses challenges in determining elastic parameters through triaxial testing.A Python script is developed to automate the calculation of elastic modulus and Poisson's ratio,over-coming subjectivity in selecting the linear portion of stress-strain curves.The script optimally identifies the linear region by minimizing the fit error with appropriate constraints,ensuring a more objective and standardized approach.The proposed methodologies are demonstrated using limestone specimens from Central Asian gas fields.These innovations offer faster,more reliable results,reducing error and enhancing the comparability of analyses in geomechanics,with potential applications across various geological settings.展开更多
The phase field model can coherently address the relatively complex fracture phenomenon,such as crack nucleation,branching,deflection,etc.The model has been extensively implemented in the finite element package Abaqus...The phase field model can coherently address the relatively complex fracture phenomenon,such as crack nucleation,branching,deflection,etc.The model has been extensively implemented in the finite element package Abaqus to solve brittle fracture problems in recent studies.However,accurate numerical analysis typically requires fine meshes to model the evolving crack path effectively.A broad region must be discretized without prior knowledge of the crack path,further augmenting the computational expenses.In this proposed work,we present an automated framework utilizing a posteriori error-indicator(MISESERI)to demarcate and sufficiently refine the mesh along the anticipated crack path.This eliminates the need for manual mesh refinement based on previous experimental/computational results or heuristic judgment.The proposed Python-based framework integrates the preanalysis,sufficient mesh refinement,and subsequent phase-field model-based numerical analysis with user-defined subroutines in a single streamlined pass.The novelty of the proposed work lies in integrating Abaqus’s native error estimation and mesh refinement capability,tailored explicitly for phase-field simulations.The proposed methodology aims to reduce the computational resource requirement,thereby enhancing the efficiency of the phase-field simulations while preserving the solution accuracy,making the framework particularly advantageous for complex fracture problems where the computational/experimental results are limited or unavailable.Several benchmark numerical problems are solved to showcase the effectiveness and accuracy of the proposed approach.The numerical examples present the proposed approach’s efficacy in the case of a complex mixed-mode fracture problem.The results show significant reductions in computational resources compared to traditional phase-field methods,which is promising.展开更多
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R513),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive data.Traditional detection methods often fail to keep pace with the evolving sophistication of cyber threats.This paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack detection.Our approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in performance.It demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant loads.Furthermore,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time applications.Designed for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current defenses.This innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.
文摘Triaxial testing serves as a fundamental method for evaluating the elastic and strength properties of rocks,crucial for developing accurate 3D geomechanical models.This paper presents a novel method for determining strength parameters by incorporating the dependence of uniaxial compressive strength(UCS)on P-wave velocity into the Hoek-Brown criterion.Additionally,a new approach is introduced to process triaxial test data efficiently using Python libraries such as SciPy,NumPy,Matplotlib,and Pandas.Furthermore,the paper addresses challenges in determining elastic parameters through triaxial testing.A Python script is developed to automate the calculation of elastic modulus and Poisson's ratio,over-coming subjectivity in selecting the linear portion of stress-strain curves.The script optimally identifies the linear region by minimizing the fit error with appropriate constraints,ensuring a more objective and standardized approach.The proposed methodologies are demonstrated using limestone specimens from Central Asian gas fields.These innovations offer faster,more reliable results,reducing error and enhancing the comparability of analyses in geomechanics,with potential applications across various geological settings.
文摘The phase field model can coherently address the relatively complex fracture phenomenon,such as crack nucleation,branching,deflection,etc.The model has been extensively implemented in the finite element package Abaqus to solve brittle fracture problems in recent studies.However,accurate numerical analysis typically requires fine meshes to model the evolving crack path effectively.A broad region must be discretized without prior knowledge of the crack path,further augmenting the computational expenses.In this proposed work,we present an automated framework utilizing a posteriori error-indicator(MISESERI)to demarcate and sufficiently refine the mesh along the anticipated crack path.This eliminates the need for manual mesh refinement based on previous experimental/computational results or heuristic judgment.The proposed Python-based framework integrates the preanalysis,sufficient mesh refinement,and subsequent phase-field model-based numerical analysis with user-defined subroutines in a single streamlined pass.The novelty of the proposed work lies in integrating Abaqus’s native error estimation and mesh refinement capability,tailored explicitly for phase-field simulations.The proposed methodology aims to reduce the computational resource requirement,thereby enhancing the efficiency of the phase-field simulations while preserving the solution accuracy,making the framework particularly advantageous for complex fracture problems where the computational/experimental results are limited or unavailable.Several benchmark numerical problems are solved to showcase the effectiveness and accuracy of the proposed approach.The numerical examples present the proposed approach’s efficacy in the case of a complex mixed-mode fracture problem.The results show significant reductions in computational resources compared to traditional phase-field methods,which is promising.
