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Academic literature on the topic 'Cross-site scripting; Deep learning; Real-time detection'
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Journal articles on the topic "Cross-site scripting; Deep learning; Real-time detection"
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Ayo, Isaac Odun, Williams Toro Abasi, Marion Adebiyi, and Oladapo Alagbe. "An implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learning." Bulletin of Electrical Engineering and Informatics 10, no. 5 (2021): 2442–53. http://dx.doi.org/10.11591/eei.v10i5.3168.
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Cross-site scripting has caused considerable harm to the economy and individual privacy. Deep learning consists of three primary learning approaches, and it is made up of numerous strata of artificial neural networks. Triggering functions that can be used for the production of non-linear outputs are contained within each layer. This study proposes a secure framework that can be used to achieve real-time detection and prevention of cross-site scripting attacks in cloud-based web applications, using deep learning, with a high level of accuracy. This project work utilized five phases cross-site scripting payloads and Benign user inputs extraction, feature engineering, generation of datasets, deep learning modeling, and classification filter for Malicious cross-site scripting queries. A web application was then developed with the deep learning model embedded on the backend and hosted on the cloud. In this work, a model was developed to detect cross-site scripting attacks using multi-layer perceptron deep learning model, after a comparative analysis of its performance in contrast to three other deep learning models deep belief network, ensemble, and long short-term memory. A multi-layer perceptron based performance evaluation of the proposed model obtained an accuracy of 99.47%, which shows a high level of accuracy in detecting cross-site scripting attacks.
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Tenzin Yarphel and Diksha Rani. "Cross-Site Scripting (XSS) in Web Applications: A systematic literature review." International Journal of Science and Research Archive 15, no. 2 (2025): 1658–67. https://doi.org/10.30574/ijsra.2025.15.2.1521.
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Cross-Site Scripting (XSS) continues to be a prevalent and damaging vulnerability in web applications, leading attackers to inject harmful scripts that can put personal data at risk, hijack sessions, and change website content. This research provides a comprehensive literature overview of XSS attacks that classify them as stored, reflected, and DOM-based, and discuss how these attacks have evolved as web technology advanced. Traditional detection methods such as input validation and signature-based filters are becoming less and less effective against sophisticated, evasive payloads. As a result, researchers are beginning to utilize Machine Learning (ML) and Deep Learning (DL) methods as more adaptive and intelligent detection methods. This paper reviews different ML/DL models for XSS detection and examines their methods, datasets, feature engineering methods, and metrics for performance. Also pointed out are significant problems such as class imbalance, adversarial examples, and deployment barrier. This study combines current research so that gaps can be identified and future directions described to build effective, scalable, and real-time XSS detection systems. The study also points out that intelligent automation is crucial in protecting web applications against the increasingly sophisticated threat landscape.
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Oshoiribhor, Emmanuel, and Adetokunbo John-Otumu. "XSS-Net: An Intelligent Machine Learning Model for Detecting Cross-Site Scripting (XSS) Attack in Web Application." Machine Learning Research 10, no. 1 (2025): 14–24. https://doi.org/10.11648/j.mlr.20251001.12.
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This research paper focuses on detecting Cross-Site Scripting (XSS) attacks, a prevalent web security threat where attackers inject malicious scripts into web applications to steal sensitive user data, hijack sessions, and execute unauthorized actions. Traditional rule-based and signature-based detection methods often fail against sophisticated and obfuscated XSS payloads, necessitating more advanced solutions. To address this, a machine learning-based model is developed to enhance XSS detection accuracy while minimizing false positives. The proposed approach utilizes feature extraction techniques, including Term Frequency-Inverse Document Frequency (TF-IDF) and n-grams, to analyze JavaScript payloads, while Principal Component Analysis (PCA) is employed for feature selection, reducing dimensionality and improving computational efficiency. A Logistic Regression classifier is trained on an XSS payload dataset from Kaggle, with data split into 80% for training and 20% for testing to ensure a robust evaluation. Hyperparameter tuning is performed using GridSearchCV, optimizing the model’s predictive capabilities. Experimental results demonstrate a 99.70% accuracy, with 100% recall and 99.36% precision, highlighting the model’s effectiveness in detecting XSS attacks while minimizing false alarms. The high recall score ensures all malicious payloads are identified, while the strong precision rate enhances reliability for real-world deployment. These findings underscore the potential of machine learning in strengthening web security frameworks, offering a scalable and efficient alternative to conventional detection systems. Future research should focus on enhancing resilience against adversarial attacks by integrating deep learning models such as Bidirectional LSTMs (BiLSTMs) and Transformer-based architectures. Additionally, deploying the model in real-time web security solutions could provide proactive defense mechanisms, ensuring robust protection against evolving XSS threats.
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Kalyan Manohar, Immadisetti, Dadisetti Vishnu Datta, and Lekshmi S. Raveendran. "Website Vulnerability Scanning System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43079.
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With the increasing reliance on web applications for business and personal use, ensuring website security has become a critical concern. Cyber threats such as SQL injection, cross-site scripting (XSS), malware infections, and unauthorized access pose significant risks to websites, leading to data breaches and service disruptions. This project aims to develop a comprehensive website security scanner that systematically identifies vulnerabilities and potential security risks.The proposed system integrates automated vulnerability scanning, penetration testing techniques, and real-time monitoring to detect security loopholes. Using machine learning and heuristic-based analysis, the scanner can identify malicious scripts, outdated software versions, weak authentication mechanisms, and misconfigured security policies. The system also performs network security assessments, analyzing potential DDoS (Distributed Denial-of-Service) attack risks and firewall configurations. The scanner generates detailed security reports, providing actionable insights and recommendations for website owners and administrators to mitigate risks effectively. Designed for continuous monitoring and proactive defense, the tool enhances cybersecurity resilience against evolving threats. This project contributes to web security advancements by offering an intelligent, automated, and scalable solution for safeguarding websites from cyberattacks. Keywords: Website Security | Vulnerability Scanner | Cyber Threats | SQL Injection | Cross-Site Scripting (XSS) | Penetration Testing | Machine Learning | Malware Detection | DDoS Protection | Authentication Security | Firewall Analysis | Web Application Security | Risk Assessment | Cybersecurity Resilience
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Niharika Prasanna Kumar. "An Integrated Framework for Securing Web Applications: Machine Learning-Driven XSS Detection and Network-Level Threat Mitigation." Journal of Information Systems Engineering and Management 10, no. 3 (2025): 1697–710. https://doi.org/10.52783/jisem.v10i3.8315.
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This paper presents a comprehensive framework designed to address the growing need for securing web-based platforms in today's digital age, where the internet is integral to everyday life. The increasing complexity of cyber threats necessitates advanced solutions, prompting the development of a robust framework that focuses on detecting and mitigating vulnerabilities within web applications. Specifically, this framework targets Cross-Site Scripting (XSS) vulnerabilities and inadequate HTTP header configurations, along with providing protection against SQL injection attacks. The proposed approach leverages state-of-the-art machine learning (ML) algorithms to enable proactive threat detection, enhancing the capability of organizations to identify and neutralize XSS attacks effectively. Furthermore, the framework incorporates real-time network protection mechanisms, exemplified by the integration of the pfSense firewall, to mitigate threats at the network level preemptively. This holistic approach to web security not only reinforces organizational resilience but also ensures compliance with regulatory standards and best practices, thereby reducing the risk of non-compliance and enhancing stakeholder trust. Overall, this framework represents a significant advancement in fortifying the security posture of web-based systems, enabling organizations to navigate the evolving threat landscape confidently and protect critical services and sensitive information.
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Arvind Kamboj, Chandrashekhar Moharir, and Shiva Kiran Lingishetty. "Securing Web Applications Against SQL Injection and XSS Attacks." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 5 (2025): 203–8. https://doi.org/10.51583/ijltemas.2025.140500025.
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Abstract: This paper presents a comprehensive approach to enhancing web application security by mitigating two of the most prevalent and dangerous threats: SQL Injection (SQLi) and Cross-Site Scripting (XSS) attacks. Traditional defense mechanisms such as Web Application Firewalls (WAFs) and rule-based filtering often fall short due to their static nature and limited adaptability to novel or obfuscated attack vectors. To address these shortcomings, the proposed methodology integrates machine learning-based models trained on diverse datasets to accurately detect and classify malicious inputs. Extensive experiments were conducted in both controlled and real-time environments, evaluating the system’s performance using key metrics including accuracy, precision, recall, and F1 score. The results demonstrate that the machine learning model significantly outperforms traditional methods, achieving a detection accuracy of 96.4%, with high precision and recall values, thus offering both effectiveness and efficiency. The system also exhibits scalability and adaptability, making it suitable for deployment in live web applications. This research highlights the critical role of intelligent, data-driven systems in modern cybersecurity frameworks and establishes a strong foundation for future work focused on developing proactive and resilient web application defenses.
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Cheng, Hui, Yonghui Zhao, and Kunwei Feng. "Subsurface Cavity Imaging Based on UNET and Cross–Hole Radar Travel–Time Fingerprint Construction." Remote Sensing 17, no. 12 (2025): 1986. https://doi.org/10.3390/rs17121986.
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As a significant geological hazard in large–scale engineering construction, deep subsurface voids demand effective and precise detection methods. Cross–hole radar tomography overcomes depth limitations by transmitting/receiving electromagnetic (EM) waves between boreholes, enabling the accurate determination of the spatial distribution and EM properties of subsurface cavities. However, conventional inversion approaches, such as travel–time/attenuation tomography and full–waveform inversion, still face challenges in terms of their stability, accuracy, and computational efficiency. To address these limitations, this study proposes a deep learning–based imaging method that introduces the concept of travel–time fingerprints, which compress raw radar data into structured, low–dimensional inputs that retain key spatial features. A large synthetic dataset of irregular subsurface cavity models is used to pre–train a UNET model, enabling it to learn nonlinear mapping, from fingerprints to velocity structures. To enhance real–world applicability, transfer learning (TL) is employed to fine–tune the model using a small amount of field data. The refined model is then tested on cross–hole radar datasets collected from a highway construction site in Guizhou Province, China. The results demonstrate that the method can accurately recover the shape, location, and extent of underground cavities, outperforming traditional tomography in terms of clarity and interpretability. This approach offers a high–precision, computationally efficient solution for subsurface void detection, with strong engineering applicability in complex geological environments.
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Chang, Fengtian, Guanghui Zhou, Kai Ding, et al. "A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies." Mathematics 11, no. 22 (2023): 4570. http://dx.doi.org/10.3390/math11224570.
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Resistance spot welding poses potential challenges for automotive manufacturing enterprises with regard to ensuring the real-time and accurate quality detection of each welding spot. Nowadays, many machine learning and deep learning methods have been proposed to utilize monitored sensor data to solve these challenges. However, poor detection results or process interpretations are still unaddressed key issues. To bridge the gap, this paper takes the automotive bodies as objects, and proposes a resistance spot welding quality online detection method with dynamic current and resistance data based on a combined convolutional neural network (CNN), long short-term memory network (LSTM), and an attention mechanism. First, an overall online detection framework using an edge–cloud collaboration was proposed. Second, an online quality detection model was established. In it, the combined CNN and LSTM network were used to extract local detail features and temporal correlation features of the data. The attention mechanism was introduced to improve the interpretability of the model. Moreover, the imbalanced data problem was also solved with a multiclass imbalance algorithm and weighted cross-entropy loss function. Finally, an experimental verification and analysis were conducted. The results show that the quality detection accuracy was 98.5%. The proposed method has good detection performance and real-time detection abilities for the in-site welding processes of automobile bodies.
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Cheng, Ming-Fang, Arvind Mukundan, Riya Karmakar, Muhamed Adil Edavana Valappil, Jumana Jouhar, and Hsiang-Chen Wang. "Modern Trends and Recent Applications of Hyperspectral Imaging: A Review." Technologies 13, no. 5 (2025): 170. https://doi.org/10.3390/technologies13050170.
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Hyperspectral imaging (HSI) is an advanced imaging technique that captures detailed spectral information across multiple fields. This review explores its applications in counterfeit detection, remote sensing, agriculture, medical imaging, cancer detection, environmental monitoring, mining, mineralogy, and food processing, specifically highlighting significant achievements from the past five years, providing a timely update across several fields. It also presents a cross-disciplinary classification framework to systematically categorize applications in medical, agriculture, environment, and industry. In counterfeit detection, HSI identified fake currency with high accuracy in the 400–500 nm range and achieved a 99.03% F1-score for counterfeit alcohol detection. Remote sensing applications include hyperspectral satellites, which improve forest classification accuracy by 50%, and soil organic matter, with the prediction reaching R2 = 0.6. In agriculture, the HSI-TransUNet model achieved 86.05% accuracy for crop classification, and disease detection reached 98.09% accuracy. Medical imaging benefits from HSI’s non-invasive diagnostics, distinguishing skin cancer with 87% sensitivity and 88% specificity. In cancer detection, colorectal cancer identification reached 86% sensitivity and 95% specificity. Environmental applications include PM2.5 pollution detection with 85.93% accuracy and marine plastic waste detection with 70–80% accuracy. In food processing, egg freshness prediction achieved R2 = 91%, and pine nut classification reached 100% accuracy. Despite its advantages, HSI faces challenges like high costs and complex data processing. Advances in artificial intelligence and miniaturization are expected to improve accessibility and real-time applications. Future advancements are anticipated to concentrate on the integration of deep learning models for automated feature extraction and decision-making in hyperspectral imaging analysis. The development of lightweight, portable HSI devices will enable more on-site applications in agriculture, healthcare, and environmental monitoring. Moreover, real-time processing methods will enhance efficiency for field deployment. These improvements seek to enhance the accessibility, practicality, and efficacy of HSI in both industrial and clinical environments.
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Ma, Long, Tao Zhou, Baohua Yu, Zhigang Li, Rencheng Fang, and Xinqi Liu. "Improving YOLOv7 for Large Target Classroom Behavior Recognition of Teachers in Smart Classroom Scenarios." Electronics 13, no. 18 (2024): 3726. http://dx.doi.org/10.3390/electronics13183726.
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Deep learning technology has recently become increasingly prevalent in the field of education due to the rapid growth of artificial intelligence. Teachers’ teaching behavior is a crucial component of classroom teaching activities, and identifying and examining teachers’ classroom teaching behavior is an important way to assess teaching. However, the traditional teaching evaluation method involves evaluating by either listening to the class on-site or playing back the teaching video afterward, which is a time-consuming and inefficient manual method. Therefore, this paper obtained teaching behavior data from a real smart classroom scenario and observed and analyzed the teacher behavior characteristics in this scenario. Aiming at the problems of complex classroom environments and the high similarity between teaching behavior classes, a method to improve YOLOv7 for large target classroom behavior recognition in smart classroom scenarios is proposed. First, we constructed the Teacher Classroom Behavior Data Set (TCBDS), which contains 6660 images covering six types of teaching behaviors: facing the board (to_blackboard, tb), facing the students (to_student, ts), writing on the board (writing, w), teaching while facing the board (black_teach, bt), teaching while facing the students (student_teach, st), and interactive (interact, i). This research adds a large target detection layer to the backbone network so that teachers’ instructional behaviors can be efficiently identified in complex classroom circumstances. Second, the original model’s backbone was extended with an effective multiscale attention module (EMA) to construct cross-scale feature dependencies under various branches. Finally, the bounding box loss function of the original model was replaced with MPDIoU, and a bounding box scaling factor was introduced to propose the Inner_MPDIoU loss function. Experiments were conducted using the TCBDS dataset. The method proposed in this study achieved mAP@.50, mAP@.50:.95, and recall values of 96.2%, 82.5%, and 92.9%, respectively—improvements of 1.1%, 2.0%, and 2.3% over the original model. This method outperformed other mainstream models compared to the current state of the art. The experimental results demonstrate the method’s excellent performance, its ability to identify various classroom behaviors of teachers in realistic scenarios, and its potential to facilitate the analysis and visualization of teacher classroom behaviors.
Conference papers on the topic "Cross-site scripting; Deep learning; Real-time detection"
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Rawoteea, Yaachna, and Girish Bekaroo. "RXSS Protect: A Browser Extension for Detection of Reflected Cross-Site Scripting Attacks in Real-Time Using Machine Learning." In 2024 International Conference on Next Generation Computing Applications (NextComp). IEEE, 2024. https://doi.org/10.1109/nextcomp63004.2024.10779651.
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Sethi, Monika, Jyoti Verma, Manish Snehi, Vidhu Baggan, Virender, and Gunjan Chhabra. "Web Server Security Solution for Detecting Cross-site Scripting Attacks in Real-time Using Deep Learning." In 2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1). IEEE, 2023. http://dx.doi.org/10.1109/icaia57370.2023.10169255.
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Liu, Tianlong, Yu Qi, Liang Shi, and Jianan Yan. "Locate-Then-Detect: Real-time Web Attack Detection via Attention-based Deep Neural Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/656.
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Web attacks such as Cross-Site Scripting and SQL Injection are serious Web threats that lead to catastrophic data leaking and loss. Because attack payloads are often short segments hidden in URL requests/posts that can be very long, classical machine learning approaches have difficulties in learning useful patterns from them. In this study, we propose a novel Locate-Then-Detect (LTD) system that can precisely detect Web threats in real-time by using attention-based deep neural networks. Firstly, an efficient Payload Locating Network (PLN) is employed to propose most suspicious regions from large URL requests/posts. Then a Payload Classification Network (PCN) is adopted to accurately classify malicious regions from suspicious candidates. In this way, PCN can focus more on learning malicious segments and highly increase detection accuracy. The noise induced by irrelevant background strings can be largely eliminated. Besides, LTD can greatly reduce computational costs (82.6% less) by ignoring large irrelevant URL content. Experiments are carried out on both benchmarks and real Web traffic. The LTD outperforms an HMM-based approach, the Libinjection system, and a leading commercial rule-based Web Application Firewall. Our method can be efficiently implemented on GPUs with an average detection time of about 5ms and well qualified for real-time applications.
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Li, Weichang, Yong Ma, and Damian San Roman Alerigi. "Automated Wellhead Monitoring Using Deep Learning from Multimodal Imaging." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23632-ms.
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Abstract Wellhead growth caused by temperature and pressure effects during production can lead to severe consequences, causing well integrity failure and surface equipment damage, sometimes with catastrophic incidents at huge safety risks and economic losses. In addition, it may lead to unintended emission when pipe connections are damaged. This work develops multimodal imaging and computer vision- based methods for automated wellhead equipment health monitoring, notably wellhead displacement or growth detection and quantification. Wellhead equipment is imaged at the well site using optical and/or hyperspectral cameras if available. The captured wellhead imagery or video is then fed into a computer vision system for analysis to determine the wellhead health condition such as the amount of displacement or growth, using machine learning techniques. First a set of sample wellhead images are labeled with wellhead segmentation annotation or bounding boxes. The set of sample data are then grouped randomly into training/validation/testing subsets, according to certain partition ratio. We then construct semantic segmentation and object detection models; train these models on the training and validation subsets and then apply to testing data set for performance assessment. These trained models can then be applied to new wellsite imagery from permanent monitoring to extract wellhead equipment. The extracted wellhead equipment image is compared with the baseline wellhead image and dimension for growth detection and quantification. This removes interferences from background objects, ambient lighting variations and other non-equipment related conditions. We collected over 4000 sample well-site images that contain well-head equipment, among which we have labeled a subset of 1200 samples which are randomly partitioned into 900 training samples, 150 validation and 150 testing samples. After training and validating the Mask R-CNN model on the training and validation samples, respectively, the model is then applied on the testing samples. The training and validation performance in terms of Intersection over Union (IOU) reach 89% and 78%, respectively, and the test performance achieves 75% IOU. The segmented well equipment image is then compared with the baseline. After 2D cross-registering, we have achieved highly accurate prediction of displacement. This computer vision and image driven based approach for wellhead displacement prediction has great advantage over traditional thermos-stress model-based approaches in that it can detect displacement in real-time with high accuracy.