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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Singh, Yuvraj Goel, Paranjay Aggarwal, Shubhani Chaudhary, Rajat Budhiraja, Ishan Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering Punjab India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350391725

Cross-Site Request Forgery (CSRF) remains a pervasive vulnerability in web applications, appearing regularly in the OWASP Top 10 lists, posing significant threats to user data and system integrity despite the existence of numerous rectification methodologies. This study undertakes a thorough, comprehensive analysis of the present environment of CSRF prevention systems, investigating their operational mechanisms, effectiveness, and associated limitations. Hence, this paper explores the role of CSRF tokens in the prevention of cyberattacks, including the detection of token absence and the implementation of token-based countermeasures to mitigate CSRF risks. The system model utilizing simple JavaScript and Flask Applications that aim to utilize the token-based countermeasure to identify the existence of tokens and generate and append them to requests, which ensures security of user data and system integrity. © 2024 IEEE.

关键词： CSRF attacks CSRF tokens Mitigation Prevention

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Modified Blowfish Encryption Standard with Optimization Strategy for Secured Cloud Storage with Data Dynamics

Modified Blowfish Encryption Standard with Optimization Stra...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Singh, Prabhdeep Singh, Pawan Agarwal, Abhay Kumar Kumar, Anil Computer Science and Engineering Amity School of Engineering and Technology Lucknow India Computer Science and Engineering Kamla Nehru Institute of Technology Sultanpur India Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India

ISBN: (纸本)9798350382693

Organizations and individuals can store, transport, and backup a rising quantity of data at an affordable cost through the assistance of cloud storage service providers, who also give them access to other cloud services. Cloud service providers use the most popular separation approach to offer effective data storage as it reduces upload capacity and storage expenses by allowing the storage of only one instance of data while eliminating multiple copies. When customers upload data to the cloud, their primary concerns are related to data security, integrity, privacy, and confidentiality. It is not advised to use traditional encryption, which is typically used to encrypt data when outsourcing, since it interferes with the data dynamics technique. This study suggested a new security architecture for cloud data storage using modified blowfish encryption to rectify these kinds of problems. Secure storage, outsourcing, and the proxy-re-encryption phase are the three stages. During the secure storage phase, a modified version of the blowfish technique is suggested for data encryption. The Kookaburra optimization algorithm is utilized for obtaining the optimal key. Auditing, proof, verification, File Access request, and file download are the five processes that occur throughout the outsourcing phase. Ultimately, the final stage of the suggested work is proxy re-encryption. In this case, the data user requests a key for a proxy, where the data owner has already transmitted the encrypted data key and the proxy provides the data user access to the encoded key. Lastly, a comparison of the suggested approach with various security methods validates its superiority. © 2024 IEEE.

关键词： Cloud storage

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Privacy-preserving filtering, control and optimization for industrial cyber-physical systems

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science China(Information sciences) 2025年第4期68卷 267-283页

作者： Derui DING Qing-Long HAN Xiaohua GE Xian-Ming ZHANG Jun WANG Department of Control Science and Engineering University of Shanghai for Science and Technology School of Engineering Swinburne University of Technology Department of Computer Science City University of Hong Kong

Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of control engineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.

关键词： industrial cyber-physical systems privacy preservation distributed control distributed optimization power systems

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Predictive Data Analysis to Support Decision-Making Based on Long-Term Impacts of Disasters 1

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

作者： Mishra, Shailendra Kumar Rahamatkar, Surendra Amity School of Engineering and Technology Department of Computer Science and Engineering Amity University Chhattisgarh Raipur India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

Research into disaster relief is a field with a lot of potential, and predictions made about its future are based on historical data. It is a difficult task to analyze the long-term consequences of a disaster. Governments, NGOs, and other organizations struggle to make sense of disaster data. It is hard to assess the impact of different factors, such as education, employment, agriculture, and the social sector, on people living in affected areas. This research paper represents a framework to evaluate the long-term effects of disasters, by means of machine learning algorithms. The algorithms mentioned in this paper are Random Forest, Decision Tree, Gradient-boosted Decision Tree (GBDT), and XGBoost. It seeks to analyze historical disaster data, identify impacts, and predict the effects of similar disasters in the future. The framework also suggests preemptive measures and the impact on those involved. Finally, the proposed framework will be able to make long-term analyses and recommendations, using data from past events. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Disasters

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C-SPIN: Classification of Satellite Images for Prediction Network 14

C-SPIN: Classification of Satellite Images for Prediction Ne...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Kumar, Sumit Mittal, Devanshu Nehra, Vibha Amity School of Engineering and Technology Department of Computer Science Engineering Noida India

ISBN: (纸本)9798350344837

Satellite image classification and prediction are crucial, with applications in domains ranging from environmental monitoring to urban planning, geological exploration, mapping, disaster response management, and agriculture. The paper provides a comprehensive analysis of current methods and techniques for object classification and prediction through satellite image data. The current work introduces the Classification of Satellite Images for Prediction Network (C-SPIN), which is meant to use both the spatial-geographical and temporal information inherent in satellite data. Image resolutions, compression, bit depth, sensor types, and spectral bands are among some of the variables, datasets, and factors considered in the research. Accurate object classification, detection, and prediction from satellite images are crucial for realising the full potential of the proposed model. The study gives insight on the strengths and limits of current models in various circumstances and highlights the potential capabilities of C-SPIN to enhance satellite image processing, image classification, and object prediction. © 2024 IEEE.

关键词： Forecasting

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Live Audio Recognition and Text Conversion System 2

Live Audio Recognition and Text Conversion System

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Sharma, Pratyaksha Gupta, Sumita Bhavya, Boddu Asmitha Sharma, Khushi Dhir, Saru Gambhir, Sapna Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP India Department of Information Technology Amity School of Engineering & Technology Amity University UP India Dept. of Computer Science College of Engineering and Computing George Mason University United States

ISBN: (纸本)9798331530389

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further. © 2025 IEEE.

关键词： Hidden Markov models

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Machine Learning Classification of Shoulder Pain Pathologies: A Scoping Review 15

Machine Learning Classification of Shoulder Pain Pathologies...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shukla, Garima Bhagat, Khushi Chaudhari, Vedant Navaneeth Krishnan, E. Bhadauria, Mansi Chauhan Chouksey, Yash Amity University Mumbai Amity School of Engineering and Technology Department of Computer Science and Engineering Mumbai India

ISBN: (纸本)9798350370249

Shoulder problem is a widely known musculoskeletal disorder that impacts heavily on the life of patients. Effective treatment for this must be preceded by accurate and timely diagnosis but traditional diagnostic techniques have always been subjective and less reliable. In recent years, machine learning (ML) techniques have shown promising results in improving the accuracy of diagnosing shoulder pain, addressing the limitations of traditional diagnostic methods. Nonetheless, there is a failure to translate these ML models into practical clinical tools that can easily be employed by healthcare professionals. In addition, this paper will present an overview of current understanding and methodologies employed for diagnosing and managing shoulder pain along with reviewing the use of ML techniques in medical image diagnostics. It also suggests development of a user-friendly and accessible diagnosis tool for shoulder pathologies aimed at linking modern research and daily practice in the medical field. © 2024 IEEE.

关键词： Patient treatment

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Enhancing Reliability in Mobile Ad Hoc Networks (MANETs) Through the K-AOMDV Routing Protocol to Mitigate Black Hole Attacks

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SN computer science 2024年第2期5卷 263页

作者： Kaushik, Sheetal Tripathi, Khushboo Gupta, Rashmi Mahajan, Prerna Amity School of Engineering and Technology Amity University Haryana Gurugram 122413 India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Haryana Gurugram 122413 India IITM Janakpuri New Delhi India

A Mobile Ad Hoc Network (MANET) is a self-organize assemblage of mobile nodes without the use of pre-existing infrastructure. They face challenges of security, routing efficiency, and network stability due to dynamic topology and limited resources. The Black Hole Attack on MANETs is a critical concern, affecting communication reliability. This malicious activity involves a node falsely advertising the shortest route to the destination, leading data packets to be routed into a “black hole” where they are dropped and causing severe disruptions. This research focuses on the Ad Hoc On-Demand Multi-Path Distance Vector Routing (AOMDV) protocol, which is preferred for its improved efficiency compared to a single-path routing protocol in MANETs. We observe, investigate, and estimate wireless ad-hoc network route optimization by reducing packet hops between nodes. We suggested a novel strategy in this paper, the K-AOMDV protocol that uses K-means clustering to prevent routing misbehavior. The efficiency of the proposed K-AOMDV (KNN-Ad-hoc on demand multi-path distance vector) routing protocol is calculated using supervised machine learning approach to predict optimal routes with delay and attacks. By employing multiple paths and dynamic route discovery, it ensures robust data delivery even in the presence of malicious nodes. This protocol’s adaptability and multi-path nature effectively minimize the effects of Black Hole Attacks, bolstering the MANETs security. Proposed algorithm has a high accuracy rate of 0.99%, 80% true positives, and 80% recall. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Black hole attack K-AOMDV K-nearest neighbors Machine learning MANET Support vector machine

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Multi-Scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-Charge in Battery Energy Storage Systems

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 405-414页

作者： Hao Liu Fengwei Liang Tianyu Hu Jichao Hong Huimin Ma School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina School of Mechanical Engineering University of Science and Technology BeijingBeijingChina IEEE

Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric *** overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical *** correlation analysis is first employed to identify SOC-related *** parameters are then input into a multi-layer GRU for point-wise feature ***,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time ***,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are *** extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction.

关键词： Electric vehicle battery energy storage system(BESS) state-of-charge(SOC)prediction gated recurrent unit(GRU) multi-scale fusion(MSF).

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