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Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting(XSS)Attack Detection

computers, Materials & Continua 2024年第10期81卷 707-748页

作者： Noor Ullah Bacha Songfeng Lu Attiq Ur Rehman Muhammad Idrees Yazeed Yasin Ghadi Tahani Jaser Alahmadi School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430073China Department of Computer Science and Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain12555Abu Dhabi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

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.

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

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An Efficient Key Establishment and Distribution Scheme for Wireless Body Area Networks

An Efficient Key Establishment and Distribution Scheme for W...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Taofeek, Yusuf Victor, Waziri Onomza Olalere, Morufu Abdullahi, Muhammad Bashir Federal University of Technology Cyber Security Science Department Minna Nigeria Federal University of Technology Computer Science Department Minna Nigeria

ISBN: (纸本)9798350358155

Secure communication in wireless body area network (WBAN) depends on efficient key management system. However, to ensure robustness, key management schemes must not only be lightweight but also support the establishment of communication keys efficiently in this paper, an efficient key management scheme is proposed based on elliptic curve cryptography (ECC) algorithm. The proposed scheme adopts a novel authentication mechanism base on authentication tables We analyzed and compare the resilience and performance of our work with concurrent schemes in terms of computation, storage and communication overheads. The results showed an improvement in our scheme in comparison with related work as displayed in table 4. Therefore, WBAN as an innovative part of the industry 4.0 have revolutionized the health-care industry. The application of the technology has transformed laboratory based medical diagnosis and data collections such as blood pressure, glucose level and blood oxygen through remote monitoring, sensing and transmission to health-care service provider for diagnosing different medical treatments. Thus, the design of such critical system requires secure and efficient technique as proposed in this work. © 2024 IEEE.

关键词： Authentication Protocol

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Enhancing Computational Model for Human Motion Recognition Using Deep Learning

Enhancing Computational Model for Human Motion Recognition U...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Yadav, Ram Kumar Daniel, A. Kumar, Birendra Sakhare, Apeksha Moriwal, Rahul Pawar, Raju Amity university Gwalior Department of Computer Science and Engineering Gwalior India G H Raisoni college of engineering Department of Data Science Internet of Things and Cyber Security Nagpur India G H Raisoni college of engineering Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350379525

Human motion recognition (HMR) is a fundamental task in computer vision with applications in healthcare, surveillance, human-computer interaction, and intelligent environments. This paper proposes a better-performing model for HMR using VGG16 and starts the preprocessing on the diverse dataset of RGB images. Our meticulous process involves normalization, resizing, and then extracting images. These preparatory steps lay the foundation for robust model training. Exploration then takes two VGG16, EfficientNetB7 and ensemble VGG16-EfficientNet B7 deep learning architectures. © 2024 IEEE.

关键词： Motion estimation

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A Literature Review on Predictive Classification of Major Depressive Disorder Using Advanced Data Analytics Techniques 5th

A Literature Review on Predictive Classification of Major De...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Mahender, Udutala Arivalagan, S. Sathiyasuntharam, V. Sudhakar, P. Annamalai University Tamilnadu Chidambaram India Department of Computer Science and Technology Annamalai University Tamilnadu Chidambaram India Department of Cyber Security CMR Engineering College Hyderabad India Department of Computer Science and Engineering Annamalai University Tamilnadu Chidambaram India

ISBN: (纸本)9789819780426

Major Depressive Disorder (MDD) is a significant global mental health concern predicted to become the leading mental health condition by 2030. It is characterised by persistent sadness, hopelessness, and a lack of interest in daily activities. The current diagnostic procedures for MDD are often unreliable, subjective, and prone to biases, emphasising the urgent need for accurate prediction methods. Predicting MDD is crucial for various reasons: it enables early detection, including identifying milder forms of depression, facilitating timely interventions and preventing the condition from worsening. Additionally, accurate prediction contributes to a deeper understanding of the disorder's neurobiological and psychological underpinnings, potentially leading to more effective treatments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Streamlining Kubernetes Deployments through GitOps Methodologies

Streamlining Kubernetes Deployments through GitOps Methodolo...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Kurrewar, Samiksha Dhomane, Sejal Dahake, Aditya Yadav, Ram Kumar Wyawahare, Nikhil Morris, Nekita Chavhan Ghrce IoT and Cyber Security Department of Data Science Nagpur India Manipal University Department of Data Science and Engineering Jaipur India

ISBN: (纸本)9798331529833

A clear process for automating the deployment of apps on Kubernetes using GitOps concepts is shown, with an emphasis on tools like Helm and Argo CD. The method demonstrates how to improve the effectiveness and reliability of application delivery by utilizing techniques like infrastructure- as-code, YAML configuration files, and CI/CD pipelines through GitHub Actions. The entities can accomplish reliable and repeatable deployments while developing teamwork among development teams by considering Git as the primary source of information. Applications remain in their original state with the support of Argo CD's continuous delivery, and monitoring tools provide data that is insightful on resource use and performance. By optimizing the management of cloud-native apps, our research helps people remain effective in the face of rapidly evolving technology. All things considered, the results demonstrate how applying GitOps techniques can streamline deployment procedures and improve application management in Kubernetes systems. © 2025 IEEE.

关键词： Information management

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Deployment of Co-operative Farming Ecosystems Using Blockchain 22nd

Deployment of Co-operative Farming Ecosystems Using Blockcha...

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on Information Assurance and security, IAS 2022

作者： Mahapatra, Aishwarya Gupta, Pranav Swarnkar, Latika Gupta, Deeya Kar, Jayaprakash Centre for Cryptography Cyber Security and Digital Forensics Department of Computer Science & Engineering Department of Communication & Computer Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9783031274084

Blockchain has helped us in designing and developing decentralised distributed systems. This, in turn, has proved to be quite beneficial for various industries grappling with problems regarding a centralised system. So, we thought of exploring blockchain’s feasibility in the agricultural industry. India is a country where a large part of the population is still dependent on agriculture, however, there’s no proper system in use as yet that can help the farmers get the right price for their farm products and help the consumers get an affordable price for their ***, we propose a blockchain based decentralized marketplace where we will implement a collaborative model between farmers and consumers. This model will allow the farmers to record their potential crops and the expected output on decentralised ledger besides, enabling them to showcase their integrity and credibility to consumers. The consumers, on the other hand, can actually check everything about the farmers with the help of their information based on the previous supplies. This open and full proof digital market framework will thus reduce black marketing, hoarding, adulteration, etc. In this research paper, we have explored one possible blockchain model by creating a breach proof ledger of records. We have used solidity and ethereum frameworks for the working of the model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Deformable Dual Graph Aggregation Transformer Convolutional Networks with Spider Wasp Optimizer for Ovarian Tumor Classification Using Magnetic Resonance Imaging

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Biomedical Materials and Devices 2025年 1-19页

作者： Shanmugaveni, V. Jotheeswari, M. Abarnaswara, R. VijayaKumar, M. Manojanani, A. Department of Computer Science and Engineering (Cyber Security) Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Artificial Intelligence & Data Science Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering RVS Technical Campus Tamil Nadu Coimbatore India

The diagnosis of Ovarian Tumor (OT) remains a significant challenge as there is presently no practical non-invasive technique to determine true benign or malignant lesions before treatment. This study proposes a unique Deformable Dual Graph Aggregation Transformer Convolutional Network (DeDGATCNet) with Spider Wasp Optimizer (SWO) for the classification of ovarian tumors acquired from magnetic resonance imaging (MRI). In contrast to traditional deep learning models, DeDGATCNet + SWO directly integrates bending graph-based forming transformer convolutional learning and improves representation for better classification. The SWO is combined to optimize the parameters of the network, thereby improving its performance. The procedures include pre-processing the MRI data from the study of ovarian tumor TCGA-OV datasets with an Adaptive Bilateral Texture Filter (ABTF) for removing artifacts and normalization of intensity, accompanied by a segment using a Masked-Attention Mask Transformer (MAMT). Feature extraction is achieved through a One-Dimensional Quaternion Discrete Fourier Transform (1D-QDFT), which captures features from both the spatial and frequency domains. DeDGATCNet + SWO performance is way ahead and sets a new accuracy record of 99.9% for state-of-the-art models. This research introduces a new type of hybridization between deformable graph transformers, quaternion-based spectral analysis, and nature-inspired optimization techniques, setting non-invasive OT at a new height for classification and improving early diagnosis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adaptive bilateral texture filter Deformable dual graph aggregation transformer convolutional networks Masked-attention mask transformer One-dimensional quaternion discrete fourier transform

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Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis

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SN computer science 2024年第6期5卷 756页

作者： Ganesh, V.N. Gulappagol, Laxmi Pushparani, M.K. Ganesh, K. Department of Electronics and Communication Engineering Alvas Institute of Engineering and Technology Department of Internet of Things and Cyber Security including Block chain Technology A J Institute of Engineering and Technology Department of Computer Science & amp Design Alvas Institute of Engineering and Technology

Early and accurate diagnosis of melanoma, a potentially life-threatening skin cancer, is crucial for improving patient outcomes. In this study, we propose a novel approach for melanoma detection, termed Panoptic Region Slicing Segmentation (PRS2) using an optimized convolution neural network (PRS2-OCNN) based on AlexNet. The proposed system integrates several advanced methods to enhance the accuracy and efficiency of melanoma identification. The initial step involves preprocessing the dermoscopic image using a 2D Fusion Filter, which enhances the image quality and prepares it for subsequent analysis. Next, the Panoptic Region Slicing Segmentation (PRS2) method is applied to emphasize the boundary regions, allowing for precise localization of melanoma-affected areas. To assess color variations within the segmented regions, we employ Threshold Histogram Evaluation (THE), which effectively characterizes melanoma-specific color patterns. The features extracted using Spread Spectral Menzies’s Feature Selection (SSMFS), reducing the dimensionality and improving the efficiency of the subsequent analysis. The core of our proposed approach lies in the optimized convolution neural network, derived from the influential AlexNet architecture. By fine-tuning the AlexNet-OCNN on the reduced feature set, we maximize its ability to accurately classify melanoma lesions based on their risk level. The PRS2-OCNN identifies melanoma classes according to their risk severity, aiding dermatologists in making informed decisions for timely and appropriate *** evaluations were conducted on a diverse and extensive dataset of dermoscopic images. The proposed system demonstrated superior performance compared to existing methods, exhibiting heightened detection accuracy by deeply analyzing the melanoma-affected regions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 2D fusion filter AlexNet Early diagnosis Melanoma Panoptic region slicing segmentation PRS2-OCNN

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Fake News Detection on Social Media Using a Stacking Model 5th

Fake News Detection on Social Media Using a Stacking Model

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Ramesh, G.S. Supriya, K.H.S. Akash, P. Rukmananda Reddy, A. Tejaswini, V. Dharmireddi, Srinivasarao Department of Computer Science and Engineering VNR VJIET Telangana Hyderabad India Department of Cyber Security MasterCard St. LouisMO63304 United States

ISBN: (纸本)9789819997060

Fake news has existed since long before the Internet was invented. It can be defined as "fictitious articles deliberately fabricated to deceive readers". While some news may have a grain of truth, it frequently lacks any contextual information. They may not contain any sources which can be verified. Some of the reports may contain straightforward factual statements, but they are intentionally worded in an inflammatory sense, often omitting crucial information, or just give one point of view. We tried to understand the nuances in fake news in this research paper by its language characteristics. Initially the news articles content was pre-processed using techniques like Lemmatization and TF-IDF. The data was then classified using a Stacking Classifier, using Naïve Bayes, SVM, and Decision Tree is used as the base models and Logistic Regression is used as Metamodel. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Vision-based UAV Detection in Complex Backgrounds and Rainy Conditions 2

Vision-based UAV Detection in Complex Backgrounds and Rainy ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Pranita Gupta, Keshav Jain, Amit Kumar Vishakha Jain, Abhishek Jain, Arpit Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Grd Institute of Management and Technology Department of Computer Science and Engineering Dehradun India Coer University Department of Cyber Security Roorkee India Uttaranchal University Department of Computer Science and Engineering Dehradun India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350371055

Studies aimed at detecting UAVs in real time using processer vision and deep learning are in their infancy. Although there are many possible advantages to using unmanned aerial vehicles (UAVs), some people are concerned about the risks and misuse of these machines. All potential threats to safety, privacy, and security fall under this category. It is common practice to combine hardware components, such as cameras, with software components when developing vision-based detection systems. Under difficult circumstances such as changing experiences, varying UAV sizes, complicated related settings, and light to heavy rain, this research evaluates the performance of popular and cutting-edge vision-based object-identification algorithms in detecting unmanned aerial vehicles (UAVs). Two datasets were created;one utilized the sky and the other had a more complex background. The study's overarching goal was to evaluate how various methods fared in this particular context. In YOLOv5, YOLOv8, Retina Net, and Faster-RCNN, we examine and evaluate indicators with one stage and two stages. The results of this research could provide light on the performance of the chosen models in difficult settings and lead to the creation of more trustworthy UAV detection systems. © 2024 IEEE.

关键词： Cameras UAV sizes Unmanned aerial vehicles Vision-based YOLOv5 and Faster-RCNN

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