Network Big Data Research Articles

Analysis of Big Data Network Security Defense Mechanism Application of Artificial Intelligence

In the context of the close integration of computer network technology and daily life, big data network security faces many challenges. With its powerful information processing ability, this AI can effectively identify and handle potential threats in the network, thereby improving network security. This paper discusses the application of artificial intelligence (AI) in enhancing big data network security defense, especially in the threat detection link. This paper proposes a detection algorithm that combines support vector machine (SVM) and K nearest neighbor (KNN), namely SVM-KNN algorithm, for network attack detection and defense. When the hazard data is 60 under the test sample, the detection accuracy of KNN, SVM, and SVM-KNN is 86.88%, 89.51%, and 96.75% respectively; when the hazard data is 60 under the experimental sample, the detection accuracy of KNN, SVM, and SVM-KNN is 82.70%, 83.88%, and 89.26% respectively.response.

Methodology for extracting narratives from social media big data

Purpose: of the article is to present the experience in developing and testing the methodology for extracting a system of narratives on a socially significant phenomenon from authentic social network big data (using the example of narratives about COVID-19 vaccination in the Russian social network VKontakte during the pandemic).Methods: of automated data analysis were used by the tools of the PolyAnalyst analytical platform: topic modeling (PLSA method), text indexing algorithms with the sentence identification stage, clustering, data aggregation, data normalization, calculation of a quantitative index. The calculation of the measure of proximity of keywords using the Python, partial manual markup and data validation were also carried out.Results: 4.5 million messages relevant to the topic of COVID-19 vaccination published in VKontakte from 01.01.2020 to 01.03.2023 were reduced to 237 stable narratives. A popularity index was calculated for each narrative. For example, the following narrative turned out to be the most popular: “Employers put pressure on people to get vaccinated” (it was supported by 76,118 texts). As a result of the study, a dataset was obtained, including 237 narratives.Conclusions and Relevance: the developed toolkit is universal: the methodology can be adapted to any relevant topic, requiring only adjustments to the input parameters of thematic modeling. The obtained dataset is planned to be introduced into scientific circulation as an up-to-date material for studying public opinion on vaccination in Russia. The results contribute to international research on public opinion and communication in crises and can serve as a basis for practical actions aimed at improving the quality of public communications and decision-making at all levels of government.

Linear Z Score and Gaussian Radial Artificial Neural Network Big Data Analytics to Enhance Crop Yield

Linear Z Score and Gaussian Radial Artificial Neural Network Big Data Analytics to Enhance Crop Yield

Digital Forensics Readiness in Big Data Networks: A Novel Framework and Incident Response Script for Linux–Hadoop Environments

Digital Forensics Readiness in Big Data Networks: A Novel Framework and Incident Response Script for Linux–Hadoop Environments

Bridging the Gap to 6G: Leveraging the Synergy of Standardization and Adaptability

The field of wireless network and communication technology is evolving from generation to generation from 1G to 6G as of now till expected to be deployed and used by 2030. It is to succeed in 5G and bring significant improvements in terms of connectivity, speed, and size in next-generation communication technology. 6G aims to deal with the rising need for more rapid information speed, low latency, and wider network coverage. This intelligent communication is proposed to meet these demands and enable new services and applications. This review paper highlights the key enablers and challenges involved in implementing intelligent communication beyond 5G. The paper identifies the research gaps for incorporating beyond 5G communication networks and outlines the possible 6G key objectives from a flexibility standpoint. It reviews infrastructure deployment, network densification, spectrum capacity and network energy efficiency in predecessors to 6G. This paper emphasizes the need for standardization and adaptation of research areas to revolutionize 6G wireless communication, focusing on areas like, ultra massive MIMO, Terahertz Communications, Cell-Free Communications, Intelligent Reflecting Surface, Visible Light Communication, Internet of Things, Big Data management, Artificial Intelligence, and network connectivity techniques.

Image privacy protection scheme based on high-quality reconstruction DCT compression and nonlinear dynamics

The protection of digital image privacy in Big Data environment is a hot issue of increasing concern. To address the contradiction between efficiency and security for privacy protection, this paper proposes a high-quality restored image privacy protection scheme based on Discrete Cosine Transform (DCT) frequency domain compression and nonlinear dynamics. First, the RGB space of the color image is converted to YCbCr domain with 4:2:0 sampling. Then, the image is divided into sub-blocks in the spatial domain, followed by block-wise DCT and quantization into frequency domain information. Next, both Alternating Current (AC) and Direct Current (DC) coefficients of all sub-blocks are individually extracted and subjected to compression encoding. Finally, a Rubik’s Cube permutation and filtering diffusion encryption algorithm based on a nonlinear dynamical chaotic system is designed. Moreover, a mechanism for generating dynamic chaotic sequences associated with plaintext is introduced to effectively counter cryptographic attacks. The combined approach of compression and encryption significantly reduces computational complexity while improving encryption efficiency. Through experimental simulation results, the compression encryption scheme demonstrates high compression ratios as well as excellent image recovery quality while providing enhanced security against common cryptographic attacks. The work in this paper provides a preferred joint compression and encryption technical solution for digital image privacy protection in Big Data network.

Big Data Analytics Model with Deep Learning Architecture to Evaluate Live Dance Ecology Through the Internet

Dance ecology, a burgeoning field at the intersection of dance, technology, and environmental studies, relies on real-time data analysis for understanding and optimizing dance performances. This paper proposed a novel Parallel Edge Big Data Analytics (PEBDA) framework, designed to efficiently process and analyze dance movement data in real time. The proposed PEBDA model uses parallel processing in the edge computing model for the analysis of the live dance ecology. Through the parallel processing of the edge model in the network big data analytics is implemented for the estimation of the multiple nodes in the network. The PEBDA model estimates the nodes across multiple environments for the examination of the ecology in the live dance. Finally, through parallel processing classification is performed with the deep learning model for the classification of live dance ecology in the computing platform. The proposed PEBDA framework, assesses classification accuracy, precision, recall, and F1-score. The simulation analysis expressed that Node 8 consistently outperforms others, achieving exceptional accuracy and precision levels above 0.97. These findings highlight the potential of edge computing in revolutionizing dance ecology analysis, enabling enhanced real-time monitoring, decision-making, and optimization of dance performances.

Study on the Judicial Practice Dilemma of "Space Obscene"

With the rapid development of the big data network era, cybercrime has gradually emerged in people's vision. As a new form of crime, it has a high degree of concealment and demonstration. Minors, due to their immature mental development and lack of sufficient judgment, are prone to becoming victims of "sexual harassment". At present, legislation and judicial interpretation are not yet perfect, and there are many disputes in the academic community regarding the application of the law. Resolving these disputes has become an urgent task. The research methods used in this article mainly include literature analysis and case analysis. Firstly, this article provides an in-depth interpretation of the keyword "sexual harassment in the distance" from different perspectives, then it combines this phrase with actual cases in the "China Judgment Document Network" to analyze the judicial practice difficulties and explore the path. Finally, it looks forward to the future of China's rule of law construction.

Empirical asset pricing based on network big data mining and privacy protection

Empirical asset pricing based on network big data mining and privacy protection

PHYSICAL ANALYSIS OF SOCIAL DYNAMICS: A SOCIOPHYSICS PERSPECTIVE

Sociophysics is an interdisciplinary field that uses methods from the physical sciences to study human behavior and interactions. It includes mathematical and computational techniques such as big data analysis, statistical modeling, network theory, and simulations. It analyzes complex systems to understand the dynamics of society. Historically, sociophysics emerged from applying statistical mechanics and thermodynamics to social phenomena. Foundational work has modeled opinion dynamics, crowd behavior, and information diffusion in social networks, and provided insights into consensus, polarization, and social stability. The basic concepts are based on treating individuals as agents, using network theory, and applying statistical mechanics and dynamical systems. Methods include big data analysis, statistical modeling, simulations, and network analysis. Future research will aim to integrate artificial intelligence and machine learning, foster interdisciplinary collaboration, utilize real-time data, and apply findings to public policy. Sociophysics aims to improve our understanding of social systems and solve today's society's complex problems. In this study, the field of sociophysics, its historical development, studies in the literature, and methods were generally discussed.

Development and Research of Chinese-English Slang Translation Retrieval System under Internet Environment

Slang plays an important role in cross-cultural communication. It is frequently used in spoken language and constantly updated. Under the drive of new needs, intelligent integrated retrieval system came into being. Based on information platforms such as the Internet, big data network and cloud computing, intelligent retrieval technology classifies and integrates English slang and develops an "English slang translation retrieval system", which greatly reduces the search cost of users and provides convenience for English learners and cross-cultural communicators.

Get the hammer out!

This paper focuses on revealing how the interplay between algorithmic interactions and the intuitive ways humans navigate digital environments can be researched through a multi-method approach to collecting and critically examining data from online platforms. We use a case study that looks at the role that social media engagement by transnational activists, local activists and celebrities played in amplifying an offline protest by group of women in India. Grounded in a critical feminist perspective, this paper uses multiple methods to demonstrate how the amplification of local protesters work through an interplay of human action and platform algorithmics. We conduct an algorithmic ethnography involving the examination of computational systems shaping online interactions. We examine the digital emergence and recognition of the women of Shaheen Bagh as subaltern political agents/subjects. Understanding of the interplay between online and offline visibility and strategic planning is highlighted. We conduct close readings of small data clusters that emerge within big data networks. We challenge the overreliance on big data methodologies and the fetishization of in-person ethnography (Bishop 2018) over digital ethnography.

Computer network big data detection based on internet of things technology

In order to solve the problems of traditional network security intrusion detection system in detection accuracy and missed detection rate, this paper proposes a computer network data detection based on Internet of things technology. This paper designs an intrusion detection system based on data clustering analysis algorithm. The hardware part of the intrusion detection system is composed of six parts: detection module, adaptive module, control management module, risk early warning module, control access module and data acquisition module. Each module is closely connected and updated with the rule base and database in real time; In the software flow design of the system, based on the data clustering analysis algorithm, the collected original data are classified and processed, and the Mings distance is determined, revealing the difference between normal data and abnormal data, and finally realizing the accurate detection of cloud computing network intrusion behavior. The experimental results show that the proposed intrusion detection system has a higher detection accuracy for malicious data, and the average missed detection rate can be controlled below 0.3 %. Conclusionthe detection system designed in this paper has more advantages in stability performance.

Deep Learning-based Privacy-preserving Publishing Method for Location Big Data in Vehicular Networks

Deep Learning-based Privacy-preserving Publishing Method for Location Big Data in Vehicular Networks

Performance study of fuzzy C-mean clustering algorithm based on local density in network security

The development and utilization of network big data is also accompanied by data theft and destruction, so the monitoring of network security is particularly important. Based on this, the study applies the fuzzy C-mean clustering algorithm to the network security model, however, the algorithm has major defects in discrete data processing and the influence of feature weights. Therefore, the study introduces the concept of local density and optimizes the initial clustering center to solve its sensitive defects as well as empirical limitations; at the same time, the study introduces the adaptive methods of fuzzy indicators and feature weighting, and uses the concepts such as fuzzy center-of-mass distribution to avoid problems such as the model converging too fast and not being able to handle discrete data. Finally, the study does a simulation analysis of the performance of each module, and the comparison of the overall algorithm with the rest of the models. The experimental results show that in the comparison of the overall algorithm, its false detection rate decreases by 8.57% in the IDS Dataset dataset, compared to the particle swarm algorithm. Therefore, the adaptive weighted fuzzy C-Means algorithm based on local density proposed in the study can effectively improve the network intrusion detection performance.

A Comparative Study on the Aesthetics of Chinese and Russian War-themed Movies from the Perspective of Big Data

As an important component of national cinema, war-themed movies represent the strength of the national film industry and modern technology. From a historical perspective, Chinese war-themed films have been influenced by the aesthetics of Soviet cinema. This paper begins by exploring the historical origins and development of Chinese and Russian war-themed mainstream films, and conducts an aesthetic comparison from the aspects of ideological consciousness, humanistic reflection, new paradigms, and application of network big data technology. It elucidates the spiritual aspirations of Chinese and Russian war-themed films and grasps the development direction of such films in China.

Exploiting high-quality reconstruction image encryption strategy by optimized orthogonal compressive sensing

Compressive sensing is favored because it breaks through the constraints of Nyquist sampling law in signal reconstruction. However, the security defects of joint compression encryption and the problem of low quality of reconstructed image restoration need to be solved urgently. In view of this, this paper proposes a compressive sensing image encryption scheme based on optimized orthogonal measurement matrix. Utilizing a combination of DWT and OMP, along with chaos, the proposed scheme achieves high-security image encryption and superior quality in decryption reconstruction. Firstly, the orthogonal optimization method is used to improve the chaotic measurement matrix. Combined with Part Hadamard matrix, the measurement matrix with strong orthogonal characteristics is constructed by Kronecker product. Secondly, the original image is sparsely represented by DWT. Meanwhile, Arnold scrambling is used to disturb the correlation between its adjacent pixels. Following this, the image is compressed and measured in accordance with the principles of compressive sensing and obtain the intermediate image to be encrypted. Finally, the chaotic sequence generated based on 2D-LSCM is used to perform on odd-even interleaved diffusion and row-column permutation at bit-level to obtain the final ciphertext. The experimental results show that this scheme meets the cryptographic requirements of obfuscation, diffusion and avalanche effects, and also has a large key space, which is sufficient to resist brute-force cracking attacks. Based on the sparse and reconstruction algorithm of compressive sensing proposed in this paper, it has better image restoration quality than similar algorithms. Consequently, the compressive sensing image encryption scheme enhances both security and reconstruction quality, presenting promising applications in the evolving landscape of privacy protection for network big data.

Big Data Analysis and User Behavior Prediction of Social Networks Based on Artificial Neural Network

The prediction of user behavior in social networks is of great significance for understanding user dynamics, personalized recommendation, and information dissemination. With the development of artificial intelligence (AI) technology, especially the application of artificial neural networks in big data analysis, new solutions and technical means have been provided for the analysis of user behavior in social networks. This study constructs a social network user behavior prediction model based on artificial neural networks. The article first reviews related research, establishes a research framework, and then describes in detail the functional structure of the user behavior prediction system. The key data structure design is meticulously constructed and the model is built through steps such as data preprocessing module, 3D feature frame construction module, feature mapping module, and feature prediction module. In addition, the article also constructs a group behavior theme probability prediction model based on an improved encoder-decoder model, which further enhances the understanding of group behavior of users in social networks. Through comparative experiments, the model proposed in this paper demonstrates its effectiveness in predicting individual and group user behaviors. The experimental results show that the model can accurately predict the behavior patterns of users in social networks and is superior to existing methods in terms of prediction accuracy and computational efficiency. This research shows that artificial neural networks are a powerful tool for analyzing big data in social networks and for predicting user behavior. The success of the study verifies the effectiveness of the model, provides a new technical path for future analysis of user behavior in social networks, and is expected to be widely applied in areas such as personalized recommendation and information dissemination analysis.

Network Big Data Analysis with Multi-Factor Hashing Ethereum Classification (Mfh-Ec): An Approach to Develop Strategy of the Tourism Industry Under the Post-Epidemic Situation

In the tourism industry, big data has emerged as a revolutionizing the way businesses operate and travelers experience destinations. With the vast amount of data generated from online bookings, social media interactions, and mobile applications, tourism companies can gain valuable insights into traveler preferences, behavior patterns, and market trends. This paper proposes a development strategy for the tourism industry in the post-epidemic situation, leveraging network big data analysis with Multi-Factor Hashing Ethereum Classification (MFH-EC). By harnessing the power of network big data, this strategy aims to provide insights into changing traveler preferences, market dynamics, and risk factors in the wake of the pandemic. The MFH-EC model facilitates the classification and analysis of diverse factors influencing tourism development, including economic indicators, health and safety measures, environmental conditions, and traveler sentiment. Through simulated experiments and empirical validations, the effectiveness of the proposed strategy is assessed, demonstrating significant improvements in predictive accuracy and decision-making capabilities. For instance, the MFH-EC model achieved an 80% accuracy rate in predicting tourism demand shifts and a 30% reduction in forecasting errors compared to traditional methods. These results underscore the potential of network big data analysis with MFH-EC in guiding strategic decision-making and fostering sustainable recovery and growth in the tourism industry post-epidemic.

Optimization of Piano Performance Teaching Mode Using Network Big Data Analysis Technology

To effectively avoid subjective bias in manual evaluation. This article proposes a MIDI piano teaching performance evaluation method based on bidirectional LSTM. This method utilizes a three-layer bidirectional LSTM neural network mechanism to make it easier for the model to capture useful information. In addition, the Spark clustering training model is constructed using the deeplearning4j deep learning framework, and the model parameters are adjusted through the UI dependency relationships provided by deeplearning4j to improve work efficiency. The experimental results verified the superiority of the bidirectional LSTM model. The methods provided in this article can improve students' independent practical abilities and reduce the pressure on teachers during the teaching process. These measures can promote the development of music education, improve students' music literacy and learning skills, and make positive contributions to the music education industry.