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
Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable...Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable.Existing methods,such as map-based navigation or site-specific fingerprinting,often require intensive data collection and lack generalization capability across different buildings,thereby limiting scalability.This study proposes a cross-site,map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge.The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes,facilitating accurate localization in unseen environments.Evaluation across two validation sites demonstrates the framework’s effectiveness.In crosssite testing(Site-A),the Transformer achieved a mean localization error of 9.44 m,outperforming the Deep Neural Network(DNN)(10.76 m)and Convolutional Neural Network(CNN)(12.02 m)baselines.In a real-time deployment(Site-B)spanning three floors,the Transformer maintained an overall mean error of 9.81 m,compared with 13.45 m for DNN,12.88 m for CNN,and 53.08 m for conventional trilateration.For vertical positioning,the Transformer delivered a mean error of 4.52 m,exceeding the performance of DNN(4.59 m),CNN(4.87 m),and trilateration(>45 m).The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration,providing stable,sub-12 m horizontal accuracy and reliable vertical estimation.This capability makes the framework suitable for real-time applications in smart buildings,emergency response,and autonomous systems.By utilizing multipath reflections as an informative structure rather than treating them as noise,this work advances artificial intelligence(AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.展开更多
<span><span><b><span style="font-family:"">Purpose:</span></b></span></span><span><span><span><span style="font-family:"&qu...<span><span><b><span style="font-family:"">Purpose:</span></b></span></span><span><span><span><span style="font-family:""> Linac quality assurance (QA) can be time consuming involving set up, execution, analysis and subject to user variability. The purpose of this study i</span></span></span></span><span><span><span><span style="font-family:"">s to develop qualitative automation tools for mechanical and imaging QA to improve efficiency, consistency, and accuracy. <b>Methods and Materials: </b>Traditionally QA ha</span></span></span></span><span><span><span><span style="font-family:"">s</span></span></span></span><span><span><span><span style="font-family:""> been performed with graph paper, film, and multiple phantoms. Analysis consists of ruler and vendor provided software. We have developed a single four-phantom<b> </b>method for QA procedures including light-radiation coincidence, imaging quality, table motion and Isocentricity an</span></span></span></span><span><span><span><span style="font-family:"">d separately cone beam computed tomography. XML scripts were developed to execute a series of tasks using Varian’s Truebeam Developer Mode. Non-phantom QA procedures have also been developed including field size, dose rate, MLC position, MLC and gantry speed, star shot, Winston-Lutz and Half Beam Block. All analysis is performed using inhouse MATLAB codes. <b>Results: </b>Overall time savings were 2.2 hours per Linac per month. Consistency improvements (standard deviation, STD) were observed for some tests. For example: field size improved from 0.11</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.04</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm and table motion improved from 0.17</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.12</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm. CBCT STD improved from 0.99</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.61</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm for slice thickness. No STD change was observed for Isocentricity test. We noticed an increase in STD from 0.33</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.41</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm for light-radiation coincidence test. There was a small drop in field size accuracy. Isocentricity showed an increase in measurement accuracy from 0.47</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.15</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm. Table motion increased in accuracy from 0.20</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.16</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm. <b>Conclusion: </b>Automation is a viable, accurate and efficient option for monthly and annual QA.展开更多
With the acceleration of network communication in the 5G era,the volume of data communication in cyberspace has increased unprecedentedly.The speed of data transmission will accelerate.Subsequently,the security of net...With the acceleration of network communication in the 5G era,the volume of data communication in cyberspace has increased unprecedentedly.The speed of data transmission will accelerate.Subsequently,the security of network communication data becomes more and more serious.Among them,malicious cross⁃site scripting leading to the leakage of user information is very serious.This article uses URL attribute analysis method and YARA rule to process data for cross⁃site scripting based on the long short⁃term memory(LSTM)characteristics of LSTM model.The results show that the LSTM classification model adopted in this paper has higher recall rate and F1⁃score than other machine learning methods,which proves that the method adopted in this paper is feasible.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.
基金funded by the Ministry of Science and Technology,Taiwan,under grant number MOST 114-2224-E-A49-002was received by En-Cheng Liou.
文摘Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable.Existing methods,such as map-based navigation or site-specific fingerprinting,often require intensive data collection and lack generalization capability across different buildings,thereby limiting scalability.This study proposes a cross-site,map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge.The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes,facilitating accurate localization in unseen environments.Evaluation across two validation sites demonstrates the framework’s effectiveness.In crosssite testing(Site-A),the Transformer achieved a mean localization error of 9.44 m,outperforming the Deep Neural Network(DNN)(10.76 m)and Convolutional Neural Network(CNN)(12.02 m)baselines.In a real-time deployment(Site-B)spanning three floors,the Transformer maintained an overall mean error of 9.81 m,compared with 13.45 m for DNN,12.88 m for CNN,and 53.08 m for conventional trilateration.For vertical positioning,the Transformer delivered a mean error of 4.52 m,exceeding the performance of DNN(4.59 m),CNN(4.87 m),and trilateration(>45 m).The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration,providing stable,sub-12 m horizontal accuracy and reliable vertical estimation.This capability makes the framework suitable for real-time applications in smart buildings,emergency response,and autonomous systems.By utilizing multipath reflections as an informative structure rather than treating them as noise,this work advances artificial intelligence(AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.
文摘<span><span><b><span style="font-family:"">Purpose:</span></b></span></span><span><span><span><span style="font-family:""> Linac quality assurance (QA) can be time consuming involving set up, execution, analysis and subject to user variability. The purpose of this study i</span></span></span></span><span><span><span><span style="font-family:"">s to develop qualitative automation tools for mechanical and imaging QA to improve efficiency, consistency, and accuracy. <b>Methods and Materials: </b>Traditionally QA ha</span></span></span></span><span><span><span><span style="font-family:"">s</span></span></span></span><span><span><span><span style="font-family:""> been performed with graph paper, film, and multiple phantoms. Analysis consists of ruler and vendor provided software. We have developed a single four-phantom<b> </b>method for QA procedures including light-radiation coincidence, imaging quality, table motion and Isocentricity an</span></span></span></span><span><span><span><span style="font-family:"">d separately cone beam computed tomography. XML scripts were developed to execute a series of tasks using Varian’s Truebeam Developer Mode. Non-phantom QA procedures have also been developed including field size, dose rate, MLC position, MLC and gantry speed, star shot, Winston-Lutz and Half Beam Block. All analysis is performed using inhouse MATLAB codes. <b>Results: </b>Overall time savings were 2.2 hours per Linac per month. Consistency improvements (standard deviation, STD) were observed for some tests. For example: field size improved from 0.11</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.04</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm and table motion improved from 0.17</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.12</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm. CBCT STD improved from 0.99</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.61</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm for slice thickness. No STD change was observed for Isocentricity test. We noticed an increase in STD from 0.33</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.41</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm for light-radiation coincidence test. There was a small drop in field size accuracy. Isocentricity showed an increase in measurement accuracy from 0.47</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.15</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm. Table motion increased in accuracy from 0.20</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm to 0.16</span></span></span></span><span><span><span><span style="font-family:""> </span></span></span></span><span><span><span><span style="font-family:"">mm. <b>Conclusion: </b>Automation is a viable, accurate and efficient option for monthly and annual QA.
文摘With the acceleration of network communication in the 5G era,the volume of data communication in cyberspace has increased unprecedentedly.The speed of data transmission will accelerate.Subsequently,the security of network communication data becomes more and more serious.Among them,malicious cross⁃site scripting leading to the leakage of user information is very serious.This article uses URL attribute analysis method and YARA rule to process data for cross⁃site scripting based on the long short⁃term memory(LSTM)characteristics of LSTM model.The results show that the LSTM classification model adopted in this paper has higher recall rate and F1⁃score than other machine learning methods,which proves that the method adopted in this paper is feasible.
