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data security and privacy using DNA Cryptography and AES Method in Cloud Computing 5

Data Security and Privacy using DNA Cryptography and AES Met...

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5th International conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： Kumar, Anuj GLA Univ Dept Comp Engn & Applicat Mathura India

ISBN: (纸本)9781665426428

Cloud computing has changed how humans use their technological expertise. It indicates a transition in the use of computers as utilitarian instruments with radical applications in general. However, as technology advances, the number of hazards increases and crucial data protection has become increasingly challenging due to extensive internet use. Every day, new encryption methods are developed, and much research is carried out in the search for a reliable cryptographic algorithm. the AES algorithm employs an overly simplistic algebraic structure. Each block employs the same encryption scheme, and AES is subject to brute force and MITM attacks. AES have not provided sufficient levels of security;there is still a need to put further levels of protection over them. In this regard, DNA cryptography allows you to encrypt a large quantity of data using only a few amount of DNA. this paper combines two methodologies, a DNA-based algorithm and the AES Algorithm, to provide a considerably more secure data security platform. the DNA cryptography technology and the AES approach are utilized for data encryption and decryption. To improve cloud security, DNA cryptography and AES provide a technologically ideal option.

关键词： DNA Cryptography Cloud Encryption Decryption AES data security Integrity

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Intelligent Intrusion Detection System Using Deep Learning Models 5

Intelligent Intrusion Detection System Using Deep Learning M...

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5th International conference on Trends in Electronics and Informatics, ICOEI 2021

作者： Varshney, Sumit Shikha Singhi, Shefali Sharma, Bharti Nit Kurukshetra Department of Computer Application HARYANA India

ISBN: (纸本)9781665415712

Cyber attacks are a very common issue in the modern world, and since there is a growing array of challenges in accurately detecting intrusion, this results in damage to security services, i.e. confidentiality, integrity, and availability of data. the attackers found new types of attacks day by day, first of all the type of attack should be analyzed properly with the help of IDS for the prevention of these types of attacks to offer the correct answers. IDS that play an important role in network security have three major features: first collection of data, selection of feature last is decision of engine. As data are increasing day by day due to which the attacks on the data also increases because of these increasing numbers of attacks on the data, the existing security applications are insufficient. SIDS and AIDS intrusion detection systems are separate proposed methods of intrusion detection to manage security threats. this paper has reviewed numerous deep learning algorithms that have been proposed to detect intrusion, i.e., Convolutional Neural Network, Recurrent Neural Network, Restricted Boltzmann Machine, Deep Brief Network and Auto encoder. It is designed to use IDS approach depending on a deep learning (DL) algorithm by using literature work comparisons and by providing the expertise either in intrusion detection or deep learning algorithms. © 2021 IEEE.

关键词： Intrusion detection

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Design and application of an International Trade Flow Prediction System Based on Machine Learning Algorithms

Design and Application of an International Trade Flow Predic...

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5th International conference on Machine Learning and Big data Analytics for IoT security and privacy, SPIoT 2024

作者： Yang, Wenwen Jiangxi Vocational College of Foreign Studies Jiangxi Nanchang330096 China

In the field of international trade, the role of accurate prediction of trade flows in formulating strategic plans for enterprises and optimizing policy regulation by the government cannot be ignored. this study designs and constructs an international trade flow prediction system. the overall architecture of the system covers five core modules: data collection and preprocessing, feature engineering, model construction, model training and optimization, and application deployment. the data collection and preprocessing module collects historical international trade data and processes the data uniformly. the feature engineering module uses principal component analysis to filter and construct the most representative and predictive feature combination from the data. the model construction module adopts the idea of ensemble learning, integrating the gradient boosting decision tree (GBDT) and the long short-term memory network (LSTM) algorithm. the model training and optimization module uses the Early Stopping method to train the model. the application deployment module encapsulates the prediction model that has been trained and optimized to the mature stage into a convenient and easy-to-use Web application. the system proposed in this paper is superior to the traditional system in terms of prediction accuracy. Its maximum prediction accuracy reaches 99.9%. the average prediction time of the system is 324.475 milliseconds, which is much lower than the 551.05 milliseconds of the ARIMA system. through case analysis, the application of this system in a multinational manufacturing company has helped the company optimize production planning and supply chain management, reduce operating costs, and enhance market competitiveness. © 2025 the Authors. Published by Elsevier B.V.

关键词： Production control

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2021 5th International conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2021

2021 5th International Conference on Electrical Engineering ...

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5th International conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2021

ISBN: (纸本)9781665495226

the proceedings contain 100 papers. the topics discussed include: a convolutional neural network for end to end structural prediction and lane detection for autonomous vehicle;the role of IoT in digitalizing mining sector of Bangladesh;driving range prediction of electric vehicles: a machine learning approach;impact of trace-based mobility models on the energy consumption of delay-tolerant routing protocols;best eleven forecast for Bangladesh cricket team with machine learning techniques;demand side management in a model house for better load profile in the perspective of Bangladesh;HDM-chain: a secure blockchain-based healthcare data management framework to ensure privacy and security in the health unit;and integrating cloud computing in e-healthcare: system design, implementation and significance in context of developing countries.

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Exploring Decentralized Identity Verification Systems Using Blockchain Technology: Opportunities and Challenges

Exploring Decentralized Identity Verification Systems Using ...

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Global conference for Advancement in Technology (GCAT)

作者： V. Saravanan Sumalatha A Desidi Narsimha Reddy B. Shamreen Ahamed Udayakumar K Arthi Priyadharshini M Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science Kristu Jayanti College Autonomous Bengaluru Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Chennai Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India Department of CSE Dhanalakshmi College of Engineering Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

the rapid advancement of digital technologies has underscored the critical necessity for robust and reliable identity verification systems in today's interconnected world. Traditional centralized identity management systems encounter myriad challenges, from vulnerabilities to data breaches and privacy concerns, to inefficiencies inherent in verification processes. this paper delves into the transformative potential of decentralized identity verification systems using blockchain technology as a robust solution to these pressing issues. By harnessing blockchain's immutable, transparent, and secure characteristics, decentralized identity systems not only promise enhanced privacy, security, and user control over personal data but also foster a paradigm shift towards more efficient and user-centric identity management *** study rigorously examines the foundational principles of blockchain technology and its strategic application in decentralized identity verification. It highlights key opportunities such as strengthened security measures through cryptographic protocols, enhanced user autonomy in managing and sharing personal information, and streamlined verification processes that reduce operational complexities and costs. Additionally, the paper critically evaluates significant challenges that must be addressed, including scalability concerns as blockchain networks grow, complexities in navigating regulatory landscapes designed for centralized systems, and overcoming barriers to widespread user adoption. through an exhaustive analysis of existing models, real-world case studies, and emerging trends, this paper aims to provide comprehensive insights into the feasibility and future prospects of decentralized identity verification systems. By contributing to the ongoing discourse on innovative identity management solutions in the digital age, this research seeks to pave the way for more secure, efficient, and equitable digital ecosystems.

关键词： data privacy Navigation Scalability Pressing Market research Blockchains Complexity theory security Reliability Identity management systems

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applications and Prospects of Neural Networks in Fault Detection: A Visualization Analysis Based on CiteSpace

Applications and Prospects of Neural Networks in Fault Detec...

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Pattern Recognition and Machine Learning (PRML), IEEE International conference on

作者： Kechen Wu Xingcheng Lu Yixuan Chen Nanjing University of Finance and Economics Nanjing China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

With the rapid development of industrial automation and intelligence, fault detection technology has become key to ensuring system stability and safety. Neural networks, as outstanding data processing and pattern recognition tools, have demonstrated extensive application potential in the field of fault detection. this study utilizes CiteSpace visualization software to conduct a thorough analysis of 2,211 papers on neural networks and fault detection in the Web of Science database from 2020 to 2024, aiming to reveal the research dynamics and development trends in this field. the analysis covered indicators such as authors, research institutions and research direction identifying major research hotspots including process monitoring, rolling element bearing monitoring, structural health monitoring, gas turbine performance assessment, and power system stability. the results indicate that advanced deep learning techniques, the integrated application of digital twins, and the development of the industrial Internet of things are driving fault detection towards greater intelligence. Future research is expected to focus more on algorithm optimization, model interpretability, and the enhancement of real-time monitoring technologies, while also emphasizing the importance of security and privacy protection.

关键词： Fault detection Neural networks Rolling bearings Power system stability Stability analysis Software Pattern recognition Visual databases security Turbines

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A Review of Cyber security Challenges and Mitigation Strategies in Industry 4.0 Technologies

A Review of Cyber Security Challenges and Mitigation Strateg...

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Advances in Computing, Communication Control and Networking (ICAC3N), International conference on

作者： Mayank Kumar Himanshu Gupta AIIT Amity University Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

the 4th Industrial Revolution, also known as or the Industry 4.0, has been characterized by the emergence of revolutionary technologies like robotics, computer vision, big data the analysis, and the Internet of things, or IoT for short, directly into the operations of industries. As these technologies become increasingly widespread, they also introduce new cybersecurity risks that need to be addressed. this paper provides a comprehensive review of the current literature on cyber security challenges and mitigation strategies in Industry 4.0. the research approach involved a systematic literature review of academic journals, conference proceedings, and relevant reports and studies. the review’s findings emphasize the most significant cyber security concerns impacting the Industry 4.0 movement, including data privacy and security, network security, and system integrity. the paper also identifies and evaluates the strategies used to mitigate these challenges, including encryption, access control, and network segmentation. the analysis section interprets the findings, draws meaningful conclusions and offers valuable recommendations for future research and practical applications. the research paper delivers an input to the topic of cyber security within Industry 4.0 by offering an in-depth review regarding the domain’s present circumstances and presenting prospective directions for additional research and advancement.

关键词： Access control Training Reviews Standards organizations Organizations Telecommunication traffic Fourth Industrial Revolution

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Generating Synthetic data for Deep Learning using VR Digital Twin 21

Generating Synthetic Data for Deep Learning using VR Digital...

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5th International conference on Cloud and Big data Computing, ICCBDC 2021

作者： Mukhopadhyay, Abhishek Rajshekar Reddy, G.S. Mukherjee, Imon Kumar Gopa, Gokul Pena-Rios, Anasol Biswas, Pradipta Indian Institute of Science India Indian Institute of Information Technology Kalyani India BT India BT United Kingdom

ISBN: (纸本)9781450390408

the ongoing Covid-19 pandemic has made it challenging for large scale data collection, in particular for Convolutional Neural Network (CNN)-based computer vision systems. Additionally, there are numerous circumstances where security, privacy, and limitations pertaining to the accessibility of the required equipment make it arduous to validate computer vision systems with real-world datasets. In this paper, we investigated the possibilities of using synthetic datasets, generated from Virtual Environments (VE) for training and validation of CNN models. We present two use cases where the above-mentioned circumstances play a vital role in preparing the datasets and validating the model with large-scale datasets. By developing and leveraging a three-dimensional Digital Twin (DT), we produce large scale datasets for validating social distancing in workspaces;and in the context of semi-autonomous vehicles, we evaluate how a CNN-based object detection model would perform in an Indian road scenario. © 2021 acm.

关键词： Object detection

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Research on Large-Scale data Processing and Dynamic Content Optimization Algorithm Based on Reinforcement Learning

Research on Large-Scale Data Processing and Dynamic Content ...

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5th International conference on Machine Learning and Big data Analytics for IoT security and privacy, SPIoT 2024

作者： Yang, Dishu Liu, Xingyu Khoury College of Computer Sciences Northeastern University San JoseCA95113 United States Social Science Research Institute Duke University DurhamNC27708 United States

With the advent of the era of big data, Internet services are faced with the challenges of massive data processing and content distribution. Traditional network management methods have gradually exposed their limitations when dealing with the rapidly growing user needs and complex application scenarios. therefore, this paper proposes a dynamic data scheduling and content optimization scheme based on deep reinforcement learning, which optimizes data stream processing and real-time content distribution through an agent decision model to improve system efficiency and user experience. In this paper, we construct a decision process considering multiple network states and behavior choices, design an adaptive reward mechanism, and improve the training efficiency and stability of the agent by combining the near-end strategy optimization method. At the same time, the multi-agent framework is extended to enable it to cope with complex network environments and tasks, ensuring efficient operation in the face of dynamic changes and partial observability. A large number of experimental results show that the proposed algorithm is better than the traditional method in many indexes such as delay, bandwidth management and data loss, and shows strong robustness and stability. this study provides theoretical support and practical guidance for intelligent optimization in large-scale network environment, and has a wide application prospect. © 2025 the Authors. Published by Elsevier B.V.

关键词： data streams

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Design and application of Pseudo-Random Sequence Base on Logistic Map 5

Design and Application of Pseudo-Random Sequence Base on Log...

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5th International conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2023

作者： Pan, Qiufeng Chen, Xiangkun Luo, Linbin Xu, Wenxia School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781839539848

Digital image is an important information carrier. In order to prevent information from being stolen and leading to information leakage, it’s very important to protect the digital image. this paper proposes a pseudo-random sequence based on logistic map and studies the encryption algorithm. First, use the logistic map to generate a logistic chaotic sequence, remove the previously poor chaotic terms and obtain a chaotic sequence with good chaotic properties. Second, sort the chaotic sequence according to certain rules and use the index of the sorted sequence to scramble the pixels of the plaintext image. then, diffuse the scrambled image pixel matrix. By combining chaotic sequences and using the bitwise XOR method, the distribution of grayscale values in the scrambled image is changed to further improve the encryption effect. Finally, combined with the characteristics of S-box, the pixel values of the digital image are changed and complete a further encryption process. After a series of operations such as scrambling, diffusion, and pixel value replacement, the ciphertext image has reached a good confidentiality state. this paper comprehensively evaluates the encryption effect and security of the method proposed in this paper from four aspects: key analysis, histogram analysis, correlation coefficient analysis, and information entropy analysis. It is believed that the encryption effect of the algorithm is good. © the Institution of Engineering & Technology 2023.

关键词： Cryptography

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