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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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Recommendation System using Emotion Detection 3

Recommendation System using Emotion Detection

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Susindra, S. Sweta, B. Keerthikha Shri, R. Brindha, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver in decision making. The paper proposes a new framework in which the detection of emotions becomes part of the recommendation system through advanced facial expression analysis and sentiment evaluation. The paper discusses a hybrid architecture combining emotion recognition via CNN and a collaborative filtering technique for dynamic recommendations. The study outcome demonstrated an enormous improvement in user engagement and satisfaction. It further underlines the transformative potential of emotion-aware systems. © 2025 IEEE.

关键词： Emotion Recognition

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Adaptive Frequency Enhancement Network for Single Image Deraining

Adaptive Frequency Enhancement Network for Single Image Dera...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Yan, Fei He, Yuhong Chen, Keyu Cheng, En Ma, Jikang School of Informatics Xiamen University Xiamen China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665410205

Image deraining aims to improve the visibility of images damaged by rainy conditions, targeting the removal of degradation elements such as rain streaks, raindrops, and rain accumulation. While numerous single image deraining methods have shown promising results in image enhancement within the spatial domain, real-world rain degradation often causes uneven damage across an image's entire frequency spectrum, posing challenges for these methods in enhancing different frequency components. In this paper, we introduce a novel end-to-end Adaptive Frequency Enhancement Network (AFENet) specifically for single image deraining that adaptively enhances images across various frequencies. We employ convolutions of different scales to adaptively decompose image frequency bands, introduce a feature enhancement module to boost the features of different frequency components and present a novel interaction module for interchanging and merging information from various frequency branches. Simultaneously, we propose a feature aggregation module that efficiently and adaptively fuses features from different frequency bands, facilitating enhancements across the entire frequency spectrum. This approach empowers the deraining network to eliminate diverse and complex rainy patterns and to reconstruct image details accurately. Extensive experiments on both real and synthetic scenes demonstrate that our method not only achieves visually appealing enhancement results but also surpasses existing methods in performance. The source code is available at https://***/yanfefei/AFENet. © 2024 IEEE.

关键词： HTTP

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Deep Learning-Based Enhanced Object Detection for Humanoid Robots 1

Deep Learning-Based Enhanced Object Detection for Humanoid R...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Negi, Akshit Patel, Harshita Vellore Institute of Technology School of Computer Science Engineering & Information Systems Vellore India

ISBN: (纸本)9798331520540

In the contemporary world, humanoid robots are likely to play a key role in various fields, including health care, domestic service, hospitality, business, and military and security activities. The robots are employed to assist people in accomplishing specific missions or sometimes substitute human beings in hazardous situations. To accomplish these missions effectively, humanoid robots should be very good at recognition and subsequent processing of objects. The main goal of this research is to enhance the object recognition capabilities Leverage the advancements in deep learning to provide the control of Robotis-Op3 humanoid robots. In this work, the researchers analyzed popular deep neural models, among which are VGG16 and ResNet, highly promoted for their robust object recognition ability, as well as the more recent CNN architectures such as SSD MobileNetV3. This research improves object recognition capabilities on hu-manoid robots as such to enhance performance. in different domains, allowing them to better recognize and interact with the objects around them. This development will then allow more efficient and effective utilization of humanoid robots, extending their application and contribution in society. © 2025 IEEE.

关键词： Anthropomorphic robots

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Risk Prediction of Heart Disease using Deep SHAP Techniques 2

Risk Prediction of Heart Disease using Deep SHAP Techniques

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Saranya, A. Narayan, Subhashini VIT University Vellore School of Computer Science Engineering and Information systems India

ISBN: (纸本)9798350371314

The automatic identification o f h eart d iseases is a challenging problem in the medical field, a nd i t constitutes the primary cause of death. Heart failure prediction, one of the symptoms of cardiovascular disease, has gained importance among physicians. In addition to increasing feature ranking and clinical prediction, deep learning also helps with output interpretation for medical practitioners. Therefore, the idea behind Explainable Artificial Intelligence (XAI) is to address the problem of deep learning algorithms in the healthcare domain not being explainable, in this case, and give the user knowledge about the model's inner working process in an understandable format. In our proposed work, convolutional neural networks are used to predict the disease, and the Deep SHAP model is used to visualize the model predictions with an accuracy of 0.90, sensitivity of 0.97, and F1-score of 0.86 for class 1. The results section includes metrics computed for two classes, such as recall, precision, and F1-score. © 2024 IEEE.

关键词： Health care

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SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

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Benchmarking visual SLAM methods in mirror environments

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Computational Visual Media 2024年第2期10卷 215-241页

作者： Peter Herbert Jing Wu Ze Ji Yu-Kun Lai School of Computer Science and Informatics Cardiff UniversityAbacws BuildingSenghennydd RdCardiff CF244AGUK School of Engineering Cardiff UniversityQueen’s BuildingsThe ParadeCardiff CF243AAUK

Visual simultaneous localisation and mapping(vSLAM)finds applications for indoor and outdoor navigation that routinely subjects it to visual complexities,particularly mirror *** effect of mirror presence(time visible and its average size in the frame)was hypothesised to impact localisation and mapping performance,with systems using direct techniques expected to perform ***,a dataset,MirrEnv,of image sequences recorded in mirror environments,was collected,and used to evaluate the performance of existing representative *** ORB-SLAM3 and BundleFusion appear to show moderate degradation of absolute trajectory error with increasing mirror duration,whilst the remaining results did not show significantly degraded localisation *** mesh maps generated proved to be very inaccurate,with real and virtual reflections colliding in the reconstructions.A discussion is given of the likely sources of error and robustness in mirror environments,outlining future directions for validating and improving vSLAM performance in the presence of planar *** MirrEnv dataset is available at https://***/10.17035/d.2023.0292477898.

关键词： visual simultaneous localisation and mapping(vSLAM) mirror localisation mapping reflection datase

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Design of a ML-based trust prediction model using intelligent TrustBoxes in challenged networks

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International Journal of systems, Control and Communications 2024年第3期15卷 209-234页

作者： Barai, Smritikona Kundu, Anindita Bhaumik, Parama Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India Department of Information Technology Jadavpur University Kolkata India

Challenged networks (CNs) contain resource-constrained nodes deployed in regions where human intervention is difficult. Opportunistic networks (OppNets) are CNs with no predefined source-to-destination paths. Due to their inherent properties, CNs and OppNets are highly susceptible to black-hole (BH) attacks, resulting in degraded packet-delivery ratio. In this work, an ML-based trust prediction model (MLTPM) is proposed to identify potential BH nodes in OppNets. MLTPM uses a novel function to calculate the total-trust-value (TTV) of each node. Intelligent TrustBoxes are introduced in the network to identify possible BH nodes, using TTV, along with five more node-behaviour features. TrustBoxes reduce the computational overhead of the resource-constrained nodes. Three simulated scenarios are compared - no detection, non-ML-based detection, and MLTPM, each using epidemic, prophet, and spray-and-wait routing protocols. MLTPM performs best with spray-and-wait, exhibiting about 25.21% and 80% mean improvement in delivery-ratio and dropped-message numbers respectively, compared to non-ML-based detection. An overall 12.62% improvement in delivery-ratio and 26.7% improvement in dropped messages is observed using MLTPM, compared to the above-mentioned scenarios. © 2024 Inderscience Enterprises Ltd.

关键词： HTTP

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Predicting Genetic Disorders: A Link Mining Approach 2

Predicting Genetic Disorders: A Link Mining Approach

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Sen, Madhura Rajasekaran, Rajkumar Jayaram Reddy, A. Govinda, K. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350306415

Genetic diseases are conditions caused by a spontaneous alteration or mutation in an individual's DNA. People can inherit genetic disorders from parents, which means they are born with them, even if they are not initially noticeable. On the other hand, some illnesses are not inherited and arise due to disease-causing mutations occurring during cell division. In link mining, link prediction is a crucial task. Based on attribute information and observed existing link information, link prediction predicts if there will be a link between two nodes. Link prediction is applicable not just in the sphere of social networks but also in other fields. This project primarily aims to forecast a person's probability of being diagnosed with a genetic condition. To accomplish this, elements such as the person's family history, the environment in which they live, and a variety of others will be considered. The likelihood of a person being diagnosed with a specific genetic condition can be predicted using the Firefly algorithm. If a person is genetically connected to someone having a given ailment, they are more likely to be diagnosed. Pollution, radioactive radiation, and other environmental factors may also affect the disease's incidence. Considering these factors, we suggest a method for determining the likelihood of a person being diagnosed via link prediction. © 2024 IEEE.

关键词： firefly algorithm genetic disorders healthcare link prediction social network analysis

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Feature Extraction using Hybridized Transfer Learning Approach for Oral Cancer Diagnosis 5

Feature Extraction using Hybridized Transfer Learning Approa...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Bharanidharan, N. Abhinav, Kalluri Karthikeyagopala Lathvik, Cherukuri Krishna Chethan, Muppavarapu Reddy, Metta Manikanta Karthikeya Deepak, K. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331504403

Oral cancer diagnosis at earlier stage is very crucial to decide the treatment procedure and to avoid mortality due to this malignant disease. Histopathological imaging is one among modalities widely used by the clinicians to identify the oral cancer. This research focuses on utilizing the hybridized version of transfer learning to extract the deep features from the histopathological oral images for oral cancer detection. Three transfer learning approaches namely VGG16, MobileNetV2, and ResNet50 are used to extract the features individually and then variance based filter technique is used to choose the appropriate features. The experiment is done using 439 oral cancer images and 89 normal images which are obtained from an online repository. The suggested approach offers 97% accuracy while the accuracy attained by stand-alone transfer learning models are less than 90%. © 2024 IEEE.

关键词： Lung cancer

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