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
Considering the escalating frequency and sophistication of cyber threats targeting web applications, this paper proposes the development of an automated web security analysis tool to address the accessibility gap for ...Considering the escalating frequency and sophistication of cyber threats targeting web applications, this paper proposes the development of an automated web security analysis tool to address the accessibility gap for non-security professionals. This paper presents the design and implementation of an automated web security analysis tool, AWSAT, aimed at enabling individuals with limited security expertise to effectively assess and mitigate vulnerabilities in web applications. Leveraging advanced scanning techniques, the tool identifies common threats such as Cross-Site Scripting (XSS), SQL Injection, and Cross-Site Request Forgery (CSRF), providing detailed reports with actionable insights. By integrating sample payloads and reference study links, the tool facilitates informed decision-making in enhancing the security posture of web applications. Through its user-friendly interface and robust functionality, the tool aims to democratize web security practices, empowering a wider audience to proactively safeguard against cyber threats.展开更多
Web applications have become a widely accepted method to support the internet for the past decade.Since they have been successfully installed in the business activities and there is a requirement of advanced functiona...Web applications have become a widely accepted method to support the internet for the past decade.Since they have been successfully installed in the business activities and there is a requirement of advanced functionalities,the configuration is growing and becoming more complicated.The growing demand and complexity also make these web applications a preferred target for intruders on the internet.Even with the support of security specialists,they remain highly problematic for the complexity of penetration and code reviewing methods.It requires considering different testing patterns in both codes reviewing and penetration testing.As a result,the number of hacked websites is increasing day by day.Most of these vulnerabilities also occur due to incorrect input validation and lack of result validation for lousy programming practices or coding errors.Vulnerability scanners for web applications can detect a few vulnerabilities in a dynamic approach.These are quite easy to use;however,these often miss out on some of the unique critical vulnerabilities in a different and static approach.Although these are time-consuming,they can find complex vulnerabilities and improve developer knowledge in coding and best practices.Many scanners choose both dynamic and static approaches,and the developers can select them based on their requirements and conditions.This research explores and provides details of SQL injection,operating system command injection,path traversal,and cross-site scripting vulnerabilities through dynamic and static approaches.It also examines various security measures in web applications and selected five tools based on their features for scanning PHP,and JAVA code focuses on SQL injection,cross-site scripting,Path Traversal,operating system command.Moreover,this research discusses the approach of a cyber-security tester or a security developer finding out vulnerabilities through dynamic and static approaches using manual and automated web vulnerability scanners.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Considering the escalating frequency and sophistication of cyber threats targeting web applications, this paper proposes the development of an automated web security analysis tool to address the accessibility gap for non-security professionals. This paper presents the design and implementation of an automated web security analysis tool, AWSAT, aimed at enabling individuals with limited security expertise to effectively assess and mitigate vulnerabilities in web applications. Leveraging advanced scanning techniques, the tool identifies common threats such as Cross-Site Scripting (XSS), SQL Injection, and Cross-Site Request Forgery (CSRF), providing detailed reports with actionable insights. By integrating sample payloads and reference study links, the tool facilitates informed decision-making in enhancing the security posture of web applications. Through its user-friendly interface and robust functionality, the tool aims to democratize web security practices, empowering a wider audience to proactively safeguard against cyber threats.
基金The author swould like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project Number No-R-14xx-4x.
文摘Web applications have become a widely accepted method to support the internet for the past decade.Since they have been successfully installed in the business activities and there is a requirement of advanced functionalities,the configuration is growing and becoming more complicated.The growing demand and complexity also make these web applications a preferred target for intruders on the internet.Even with the support of security specialists,they remain highly problematic for the complexity of penetration and code reviewing methods.It requires considering different testing patterns in both codes reviewing and penetration testing.As a result,the number of hacked websites is increasing day by day.Most of these vulnerabilities also occur due to incorrect input validation and lack of result validation for lousy programming practices or coding errors.Vulnerability scanners for web applications can detect a few vulnerabilities in a dynamic approach.These are quite easy to use;however,these often miss out on some of the unique critical vulnerabilities in a different and static approach.Although these are time-consuming,they can find complex vulnerabilities and improve developer knowledge in coding and best practices.Many scanners choose both dynamic and static approaches,and the developers can select them based on their requirements and conditions.This research explores and provides details of SQL injection,operating system command injection,path traversal,and cross-site scripting vulnerabilities through dynamic and static approaches.It also examines various security measures in web applications and selected five tools based on their features for scanning PHP,and JAVA code focuses on SQL injection,cross-site scripting,Path Traversal,operating system command.Moreover,this research discusses the approach of a cyber-security tester or a security developer finding out vulnerabilities through dynamic and static approaches using manual and automated web vulnerability scanners.
