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Deploying hybrid EnsembleMachine learning Techniques for Effective Cross-Site Scripting (XSS) Attack Detection

Computers, Materials and Continua 2024年第1期81卷 707-748页

作者： Bacha N.U. Lu S. Ur Rehman A. Idrees M. Ghadi Y.Y. Alahmadi T.J. School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan 430073 China Department of Computer Science and Engineering University of Engineering and Technology Lahore 54000 Pakistan Department of Computer Science and Software Engineering Al Ain University Al Ain Abu Abu Dhabi 12555 United Arab Emirates Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh 84428 Saudi Arabia

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 algorithmtuning 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 a

关键词： Cross-site scripting hybrid learning machine learning stacking ensemble learning XSS detection

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Data-driven and Model-based hybrid Reinforcement learning to Reduce Stress on Power Systems Branches

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 433-442页

作者： Mariana Kamel Renchang Dai Yawei Wang Fangxing Li Guangyi Liu The University of Tennessee.Knoxville TNUSA Puget Sound Energy BellevueWAUSA PayPal San JoseCAUSA Envision Digital.Redwood City CAUSA

This work proposes a reinforcement learning(RL)approach to tackle the control problem of branch overload relief in large power ***,a control agent is trained to change generators'real power output in order to relieve the stressed *** large power systems,this control problem becomes one whose decision space(i.e.,the action space)is both highly-dimensioned and *** makes it extremely difficult to have successful training for RL-based *** improve the effectiveness,a data-driven and model-based hybrid approach is proposed to optimize the control by combining RL-agent actions and generator shifting factor-driven ***,with the proposed approach the RL-agent successfully trains on large power *** proposed design is tested on both the IEEE 118-bus testing system and a 2749-bus real *** obtained results show that the proposed hybrid approach outperforms the data-driven training approach.

关键词： Deep deterministic policy gradient generation re-dispatch hybrid learning overload relief reinforcement learning

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Technology-Supported University Teaching Models in China During the Pandemic:National Survey and Future Prospects

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ECNU Review of Education 2024年第1期7卷 174-181页

作者：汪潇潇郭双双 Tsinghua University

●The Ministry of Education Research Center for Online Education conducted a national survey of university administrators of learning and teaching.●The survey results showed that(a)the universities in eastern China had developed and introduced more massive open online courses(MOOCs)than those in the central and western regions;(b)of the various measures for blended learning,the universities tended to focus on supporting hardware development before providing training and incentive measures to teachers;and(c)the universities had widely used hybrid learning *** relation to hybrid learning,Tsinghua University explored infrastructure upgrades,teacher training,and the optimal use of the rain classroom and its clone class,and launched the global hybrid classroom and global open courses.●In the future,it will be necessary to further promote education equity in line with national policies and provide and perfect incentives to improve the quality of blended learning and expand hybrid learning while improving interaction through technology.

关键词： Blended learning hybrid learning MOOCs

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The Impact of Music Education on Children’s Cognitive Development:From Traditional Classrooms to Modern Digital Platforms

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Cultural and Religious Studies 2024年第9期12卷 595-600页

作者： GONG Xinyi Xinghai Conservatory of Music GuangzhouChina

Music education has long been debated for its influence on children’s cognitive development,particularly regarding their thinking methods and *** article synthesizes research data to examine the cognitive benefits of music instruction,including increased IQ,language proficiency,memory,and *** face-to-face training,while personalized and socially interactive,faces limitations such as budget constraints and *** digital platforms offer individualized learning paths with AI-driven feedback but may lack necessary interpersonal *** paper proposes a hybrid approach to music education,integrating traditional and digital methods to maximize cognitive *** research is recommended to explore the implementation of these integrated learning strategies in varied educational settings.

关键词： music education cognitive development traditional teaching digital platforms hybrid learning IQ language proficiency memory attention

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Weighted learning for Feedforward Neural Networks

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Journal of Electronic Science and Technology 2014年第3期12卷 299-304页

作者： Rong-Fang Xu Thao-Tsen Chen Shie-Jue Lee Department of Electrical Engineering National Sun Yat-Sen University

In this paper, we propose two weighted learning methods for the construction of single hidden layer feedforward neural networks. Both methods incorporate weighted least squares. Our idea is to allow the training instances nearer to the query to offer bigger contributions to the estimated output. By minimizing the weighted mean square error function, optimal networks can be obtained. The results of a number of experiments demonstrate the effectiveness of our proposed methods.

关键词： Extreme learning machine hybrid learning instance-based learning weighted least squares

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人工免疫杂交学习方法及其在函数近似中的应用

人工免疫杂交学习方法及其在函数近似中的应用

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作者： SHAZLY ALSOLIBY 南昌大学

学位级别：硕士

The immune system is highly distributed, highly adaptive, self-organizing in nature, maintains a memory of past encounters, and has the ability to continually learn about new encounters. From a computational viewpoint, the immune system has much to offer by way of inspiration. Recently there has been growing interest in the use of the natural immune system as inspiration for the creation of novel approaches to computational problems;this field of research is referred as Immunological Computation (IC) or Artificial Immune Systems (AIS).Over the years, biology has provided a rich source of inspiration for many different people in many different ways ranging from designing aircraft wings to bulletproof vests. Biology has also been used as a source of inspiration for computation problems, which can be classified as biologically motivated computing. This is different from computationally motivated biology, where computing provides the source and inspiration for models in biology. There has been much work done on the use of biological metaphors, for example neural networks, genetic algorithms and genetic programming. Recently, there has been increasing interest in using the natural immune system as a metaphor for computation in a variety of domains.

关键词： hybrid learning Radial basis function neural networks Artificial immune systems Immune system Clonal selection

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