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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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Performance enhancement of deep neural network using fusional data assimilation and divide-and-conquer approach;case study: earthquake magnitude calculation

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Neural Computing and Applications 2024年第27期36卷 16899-16910页

作者： Esmaeili, Rezvan Kimiaefar, Roohollah Hajian, Alireza Soleimani-Chamkhorami, Khosro Hodhodi, Maryam Department of Physics Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran Department of Mathematics Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran

The presence of "ill-posed samples" specifically in low-volume datasets leads to accuracy decrement in the learning procedure and the generalization of neural networks. Such samples can be caused by various reasons such as noise contamination, corrupted sensors, or even, the complex distribution of physical properties governing the problem. The peak ground acceleration (PGA) datasets are definitely among the last mentioned. Focusing on speed and accuracy, a method for calculating earthquake magnitude based on the PGA data recorded at a single station along with hypocentral information has been presented in this research. Here, after training a deep neural network, the regression errors of the training data samples are clustered into two groups, namely well and ill posed using the grey wolf optimization algorithm. Instead of being removed, the data samples with low learning rates are then modified using samples selected from the other cluster in a fusional form. Then, two separate models are used and trained independently for the clusters. Next, in addition to the routine procedure of network generalization, every new sample is first checked whether is more likely to belong to which group of the clustered data, and after processing, the corresponding trained model is used. The results of the experiments show that using the proposed method results in magnitude calculation with an error order of less than 0.212 units of moment magnitude with a probability of more than 99.7%, which is superior to the conventional methods some of which were reviewed in this research. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep neural networks

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Smart architectures: computerized classification of brain tumors from MRI images utilizing deep learning approaches

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Multimedia Tools and Applications 2025年第5期84卷 2261-2292页

作者： Mijwil, Maad M. Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq

Brain tumors are complex medical conditions that appear due to the abnormal proliferation of cells. They are tumors (benign or malignant) that grow abnormally or are masses of tissue that develop in a dreadful manner in the human brain. In most cases, there is no specific or known cause for the occurrence of these tumors, and late diagnosis leads to a disproportionate progression of cancer cells or the growth of malignant masses in the brain, which leads to difficulty in performing treatment directly. Deep learning methods, most notably convolutional neural networks (CNNs), are employed to accurately diagnose and classify brain tumor images due to their ability to process large sets of complex medical images. In this paper, the role of deep learning approaches will be highlighted by utilizing different architectures of the CNNs (DenseNet-201, Inception-V1, AlexNet, and MobileNetV2), comparing their performance, and finding the most suitable solution for identifying and classifying brain tumors and solving the problem of late or incorrect diagnosis with high efficiency and accuracy. The database of this paper includes more than 3000 MRI images taken from Kaggle. During implementation, it became clear that the most suitable performance was the MobileNetV2, as it earned an accuracy of 96.5%, a sensitivity of 96.6%, a precision of 98.2%, a specificity of 96.3% and an F1-score of 97.4%. These effects demonstrate that MobileNetV2 is the best architecture among others, as it is highly capable of classifying aberrant and normal tumors from brain MRI images. This paper emphasizes that deep learning is crucial in supporting hospitals and experts in interpreting MRI images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Ensemble of deep learning techniques to human activity recognition using smart phone signals

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Multimedia Tools and Applications 2024年第42期83卷 89635-89664页

作者： Imanzadeh, Soodabeh Tanha, Jafar Jalili, Mahdi Electrical and Computer Engineering Department University of Tabriz Tabriz Iran School of Engineering RMIT University Melbourne Australia

Human Activity Recognition (HAR) has become a significant area of study in the fields of health, human behavior analysis, the Internet of Things, and human–machine interaction in recent years. Smartphones are a popular choice for HAR as they are common devices used in daily life. However, most available HAR datasets are gathered in laboratory settings, which do not reflect real-world scenarios. To address this issue, a real-world dataset using smartphone inertial sensors, involving 62 individuals, is collected. The collected dataset is noisy, small, and has variable frequency. On the other hand, in the context of HAR, algorithms face additional challenges due to intra-class diversity (which refers to differences in the characteristics of performing an activity by different people or by the same individual under different conditions) and inter-class similarity (which refers to different activities that are highly similar). Consequently, it is essential to extract features accurately from the dataset. Ensemble learning, which combines multiple models, is an effective approach to improve generalization performance. In this paper, a weighted ensemble of hybrid deep models for HAR using smartphone sensors is proposed. The proposed ensemble approach demonstrates superior performance compared to current methods, achieving impressive results across multiple evaluation metrics. Specifically, the experimental analysis demonstrates an accuracy of 97.15%, precision of 96.41%, recall of 95.62%, and an F1-score of 96.01%. These results demonstrate the effectiveness of our ensemble approach in addressing the challenges of HAR in real-world scenarios. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smartphones

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Hopping-mean: an augmentation method for motor activity data towards real-time depression diagnosis using machine learning

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Multimedia Tools and Applications 2025年第18期84卷 18781-18799页

作者： Misgar, Muzafar Mehraj Bhatia, M.P.S. Department of Computer Science & Engineering Netaji Subhas University of Technology New Delhi India

The advances from the last few decades in the fields of ML (Machine Learning), DL (Deep Learning), and semantic computing are now changing the shape of the healthcare system. But, unlike physical health problems, diagnosis of mental health problems lacks a direct quantitative test. A huge variety of data generated by wearable IoMT (Internet of Medical Things) devices (e.g. wrist actigraph) provides an efficient objective way to diagnose such mental health problems in the early stages. In this paper, we propose a novel overall methodology comprised of preprocessing, data augmentation, and classification of depressive episodes from motor activity data. A time-efficient Augmentation Algorithm is proposed based on windowing over the same timestamps to resolve the problem of data loss in the Depresjon database and the UMAP (Uniform Manifold Approximation and Projection) based feature extraction method is used on motor activity database containing variable-length activity records for 23 control patients and 32 condition groups. The classification of depression from 24 h of motor activity data with augmentation performs much better than raw data. The preprocessing approach followed by Random forest provides cut over cut over the current state of the art and provides the highest accuracy of 74.29%, specificity of 74.80%, and sensitivity of 73.56 on non-augmented data and gives the highest accuracy of 98.65%, specificity of 97.36% and sensitivity of 99.89% on augmented data. This shows the effectiveness of using the custom augmentation approach proposed in this paper. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Medical problems

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DSANet: dilated spatial attention network for the detection of text, non-text and touching components in unconstrained handwritten documents

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Neural Computing and Applications 2024年第27期36卷 16959-16976页

作者： Bhowmik, Showmik Risat, Shaikh Sarkar, Bhaskar Research Lab Department of Computer Science and Engineering Ghani Khan Choudhury Institute of Engineering and Technology Malda India

Handwritten documents generated in our day-to-day office work, class room and other sectors of society carry vital information. Automatic processing of these documents is a pipeline of many challenging steps. The very first and crucial step is to identify text separately from the non-text as any OCR (optical character recognition) engine can only process the textual content. Separating text from non-text in unconstrained handwritten documents is a very complex task. In addition to other challenges, touching component is one of the major issues for text non-text separation in unconstrained handwritten documents. Detection of text, non-text along with touching component in such documents is an unexplored area of research. To address this issue, in this work, we develop a dilated spatial attention-based network for text, non-text and touching component detection. Additionally, in this work, we also prepare a realistic dataset for the said task. In the proposed dataset, the present model obtains overall accuracy of 87.85%. The performance of the present model is compared with seven feature-engineering-based methods and six deep learning-based methods. In most of the cases, the proposed model outperforms the comparing methods in the proposed dataset. The codes of our method are available here https://***/Showmik-Bhowmik/DSANet-Dilated-Spatial-Attention-.git. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep learning

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Preserving Data Secrecy and Integrity for Cloud Storage Using Smart Contracts and Cryptographic Primitives

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computers, Materials & Continua 2024年第5期79卷 2449-2463页

作者： Maher Alharby Cybersecurity Department College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia

Cloud computing has emerged as a viable alternative to traditional computing infrastructures,offering various ***,the adoption of cloud storage poses significant risks to data secrecy and *** article presents an effective mechanism to preserve the secrecy and integrity of data stored on the public cloud by leveraging blockchain technology,smart contracts,and cryptographic *** proposed approach utilizes a Solidity-based smart contract as an auditor for maintaining and verifying the integrity of outsourced *** preserve data secrecy,symmetric encryption systems are employed to encrypt user data before outsourcing *** extensive performance analysis is conducted to illustrate the efficiency of the proposed ***,a rigorous assessment is conducted to ensure that the developed smart contract is free from vulnerabilities and to measure its associated running *** security analysis of the proposed system confirms that our approach can securely maintain the confidentiality and integrity of cloud storage,even in the presence of malicious *** proposed mechanism contributes to enhancing data security in cloud computing environments and can be used as a foundation for developing more secure cloud storage systems.

关键词： Cloud storage data secrecy data integrity smart contracts cryptography

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Fog-Enabled Multimodal Chest-Worn Device for Systolic Blood Pressure Monitoring

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IEEE Access 2025年 13卷 90345-90357页

作者： Salas, Pamela Mejia-Munoz, Jose-Manuel Gonzalez-Landaeta, Rafael BIOCIM Research Group Department of Electrical and Computer Engineering Juarez Chihuahua32310 Mexico

This paper presents a Fog-enabled chest-worn device for estimating systolic blood pressure (SBP). The device integrates two sensors that simultaneously detect the phonocardiogram (PCG), ballistocardiogram (BCG), and seismocardiogram (SCG) from a single location on the chest. These signals are good indicators of the early stages of cardiovascular diseases (CVD). Here, a new fog-based deep learning architecture that extracts features directly from raw signals to estimate the SBP is proposed. Three signals were transmitted to a fog computing system via a socket. Four models were implemented for analysis: Random Forest (RF), AdaBoost, Gradient Boosting (GB), and a novel deep learning architecture. The RF, AdaBoost, and GB models focused on the time delays between the main waves of the PCG, BCG, and SCG, while the deep learning architecture extracted features directly from the signals using convolutional layers. The tests involved 36 healthy volunteers under both resting and physically active conditions. The device was built using surface-mount components and powered by a 3.7 V, 250 mAh battery. The dimensions were 65 mm × 54 mm, and the weight was 56 g. The three cardiac signals were simultaneously detected, with a signal-to-noise ratio higher than 41 dB. Among the four implemented models for SBP estimation in fog, the proposed deep learning architecture demonstrated the best performance, achieving an MAE of 3.5 mmHg without estimating the timing between the three detected signals. Integrating the methodology discussed here, the proposed device offers a novel method for estimating blood pressure through raw biosignals detected by a wearable device. © 2013 IEEE.

关键词： Surface mount technology

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Energy efficient resource utilization and load balancing in virtual machines using prediction algorithms

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 127-134页

作者： Udayasankaran, P. Thangaraj, S. John Justin Department of Computer Science and Engineering Saveetha School of Engineering Chennai Tamilnadu India

In recent years due to increase in the number of customers and organizations utilize cloud applications for personal and professionalization become greater. As a result of this increase in utilizing the Cloud services, Cloud data centers found to be huge consumption with boundless energy and harmful materials into the environment. The Primary goal of fully virtual machines is to minimize consumption of boundless energy utilization and providing uttermost resources. Balancing the loads on the servers has become a challenging task for the cloud service provider. Load balancing on servers helps to improve the performance of the virtual machines and to minimize energy and processing time in cloud systems. Here we discuss about various load balance r serves as the single and distributes incoming traffic across multiple targets. Reducing the consumption for the data center industries, power supply capacity has more increased and demand will be continued. With an aim to reduce and bring down active server infrastructure burdens. All about, this includes various challenges are taken care of by the environment. Behavior approaches of systems and consumers are not defined clearly. We present a novel resource optimization framework to discover jobs with minimum utilization. This progress ground work encircle boundless energy utilization. © 2023

关键词： Virtual machine

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Hybrid classification of XGBoost-based ADAM optimization for coronary artery disease diagnosis

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10035-10044页

作者： Nagamani, T. Logeswari, S. Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Perundurai India Department of Information Technology Karpagam College of Engineering Tamilnadu Coimbatore India

A common cardiovascular illness with high fatality rates is coronary artery disease (CAD). Researchers have been exploring alternative methods to diagnose and assess the severity of CAD that are less invasive, cost-effective, and utilize noninvasive clinical data. Machine learning algorithms have shown promising and potential results. Accordingly, this study focuses on assisting medical practitioners with CAD detection by using a hybrid classification system combining XGBoost and Adam optimization. The primary approach incorporates One-Hot encoding to transform categorical attributes within the dataset, enhancing the precision of predictions. The secondary approach constitutes a hybrid classification model integrating XGBoost and employing Adam optimizations for CAD detections. The efficacy of the recommended method is assessed using the cleveland, Hungarian, and Statlog heart-disease data sets. The proposed system and the standard Grid and Random Search classifiers are compared. The experimental outcomes indicate that the suggested model achieves a notable prediction accuracy of 94.19%. This represents an improvement of 7 to 8% over the existing grid search algorithm and 2 to 3% improvement over the random search algorithm for the above all datasets. Hence, the proposed system can be a valuable tool for identifying CAD patients, offering enhanced prediction accuracy. © 2024 – IOS Press.

关键词： Diseases

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