With companies relying more and more on online services, the risk posed by Distributed Denial of Service (DDoS) attacks has notably increased. These attacks have the potential to cause significant damage to the system...
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With the increasing availability of travel-related data and the rising demand for personalized travel recommendations, context-aware recommender systems (CARS) are crucial for assisting travelers in discovering releva...
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The 360 apartments explorer is a mechanism that completely transforms the process of looking for and choosing real estate properties. This innovative tool provides users with a comprehensive overview of available apar...
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With the rapid development of digital technology, the global economy is undergoing an unprecedented transformation, and "Industry 4.0", as the core concept, is profoundly changing the face of manufacturing. ...
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Traffic safety remains one of the most concerning issues for humans, with people dying in traffic accidents every moment, and nearly half of them being related to fatigue driving. When drivers feel fatigued, the eyes ...
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This research offers an in-depth analysis of the integration of Autonomous Vehicle Platooning, Vehicle-to-Everything (V2X) communication, Edge Analytics, and Real-time Traffic Management. Central to the study is '...
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The identification of osseous fractures has significant importance for defining precise medical diagnosis and con- structing treatment plans, especially in emergent and orthopedic contexts. This work proposes a new cl...
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This research presents the Hybrid Retrieval-Augmented Transformer Network (HRATN), a cutting-edge deep learning algorithm crafted to significantly elevate the performance of ChatBots through an innovative approach to ...
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Time series clustering is a complex unsupervised data mining and analysis technique that can be applied to various fields such as signal processing, financial analysis, and more. However, time series data often contai...
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Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving ...
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Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving sophistication of cyber *** 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 *** 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 *** demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant ***,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 *** 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 *** innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.
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