Network Analysis Of Data Research Articles

Improved identification of network anomalies through optimal CURE clustering

Abstract In this paper, we propose an advanced network anomaly behavior identification framework to overcome the constraints inherent in conventional rule- or signature-based approaches, which often struggle with emerging and previously unknown threats. Central to our framework is an Enhanced CURE (Cluster Updating and REfining) clustering algorithm, meticulously tailored and refined to incorporate a density-based methodology. This enhancement enables the algorithm to discern subtle shifts in network anomaly patterns with heightened precision. The implementation workflow commences with the application of the optimized CURE algorithm to analyze network data, followed by the deployment of a sophisticated anomaly degree ranking mechanism. This mechanism, through meticulous calculation of individual data points' anomaly degrees and subsequent ranking, effectively isolates those deviating significantly from standard behavioral norms, incorporating a strategic threshold to filter out false positives. To validate our methodology's efficacy and its superiority over existing techniques, experiments were conducted utilizing a substantial real-world network dataset. These tests affirm not only a marked increase in the accuracy of abnormal behavior recognition and a reduction in computational intricacy but also demonstrate the adaptability across diverse network ecosystems. Our approach has proven successful in pinpointing a wide array of network anomalies, encompassing malicious cyberattacks, fraudulent activities, unauthorized intrusions, and breaches of security protocols, thereby highlighting its comprehensive capability in bolstering network defense strategies. Despite the notable advancements and successful identification of various network anomalies, our framework currently lacks integration with real-time learning capabilities, limiting its immediate responsiveness to rapidly evolving attack patterns and necessitating ongoing research for dynamic updates and adaptive learning mechanisms.

Adaptive Strategies and Sustainable Innovations of Chinese Contractors in the Belt and Road Initiative: A Social Network and Supply Chain Integration Perspective

As global economic integration and rapid technological advancements transform international business, international engineering contracting has become essential for achieving sustainable development goals (SDGs). This paper investigates the significant impact of China’s strategic initiatives, notably the “Going Global” strategy and the Belt and Road Initiative (BRI), on the operational practices of Chinese enterprises involved in overseas investments. Central to this transformation is the Engineering, Procurement, and Construction (EPC) model, which emphasizes the integration of supply chain management and stakeholder collaboration to enhance performance in international EPC projects and underscores the crucial role of these elements in promoting sustainability. Incorporating insights from social network data analysis, this study reveals that contractors collaborating with various stakeholders—such as owners/consulting engineers, domestic and foreign customs departments, and group headquarters/design parties—exhibit a high degree of similarity in personnel profiles. This suggests that the internal organizational structure and personnel allocation of contractors could be optimized to enhance operational efficiency, aligning with the collaborative patterns identified. This study addresses a critical research gap by exploring how effective supply chain management and collaborative stakeholder engagement within multinational EPC projects contribute to sustainable outcomes. Employing advanced social network analysis software, the research examines the complex interactions among stakeholders and their influence on procurement dynamics. Findings indicate that strong relational networks and strategic collaborations significantly enhance procurement efficiency and project success, underscoring the importance of supply chain integration. Ultimately, integrating supply chain management principles into the EPC model not only offers innovative perspectives for advancing sustainability in international projects but also provides actionable insights for improving project outcomes within the BRI framework. This research underscores the pivotal role of supply chain organization and stakeholder cooperation in achieving sustainability objectives, thereby enriching the discourse on sustainable enterprise operation and supply chain management in the context of global initiatives.

A Sparse Beta Regression Model for Network Analysis

For statistical analysis of network data, the β -model has emerged as a useful tool, thanks to its flexibility in incorporating nodewise heterogeneity and theoretical tractability. To generalize the β -model, this paper proposes the Sparse β -Regression Model (S β RM) that unites two research themes developed recently in modelling homophily and sparsity. In particular, we employ differential heterogeneity that assigns weights only to important nodes and propose penalized likelihood with an ℓ 1 penalty for parameter estimation. While our estimation method is closely related to the LASSO method for logistic regression, we develop a new theory emphasizing the use of our model for dealing with a parameter regime that can handle sparse networks usually seen in practice. More interestingly, the resulting inference on the homophily parameter demands no debiasing normally employed in LASSO type estimation. We provide extensive simulation and data analysis to illustrate the use of the model. As a special case of our model, we extend the Erdős-Rényi model by including covariates and develop the associated statistical inference for sparse networks, which may be of independent interest.

Advanced mathematical modeling of mitigating security threats in smart grids through deep ensemble model

A smart grid (SG) is a cutting-edge electrical grid that utilizes digital communication technology and automation to effectively handle electricity consumption, distribution, and generation. It incorporates energy storage systems, smart meters, and renewable energy sources for bidirectional communication and enhanced energy flow between grid modules. Due to their cyberattack vulnerability, SGs need robust safety measures to protect sensitive data, ensure public safety, and maintain a reliable power supply. Robust safety measures, comprising intrusion detection systems (IDSs), are significant to protect against malicious manipulation, unauthorized access, and data breaches in grid operations, confirming the electricity supply chain’s integrity, resilience, and reliability. Deep learning (DL) improves intrusion recognition in SGs by effectually analyzing network data, recognizing complex attack patterns, and adjusting to dynamic threats in real-time, thereby strengthening the reliability and resilience of the grid against cyber-attacks. This study develops a novel Mountain Gazelle Optimization with Deep Ensemble Learning based intrusion detection (MGODEL-ID) technique on SG environment. The MGODEL-ID methodology exploits ensemble learning with metaheuristic approaches to identify intrusions in the SG environment. Primarily, the MGODEL-ID approach utilizes Z-score normalization to convert the input data into a uniform format. Besides, the MGODEL-ID approach employs the MGO model for feature subset selection. Meanwhile, the detection of intrusions is performed by an ensemble of three classifiers such as long short-term memory (LSTM), deep autoencoder (DAE), and extreme learning machine (ELM). Eventually, the dung beetle optimizer (DBO) is utilized to tune the hyperparameter tuning of the classifiers. A widespread simulation outcome is made to demonstrate the improved security outcomes of the MGODEL-ID model. The experimental values implied that the MGODEL-ID model performs better than other models.

Popularity-Aware Service Provisioning Framework in Cloud Environment

To balance the tradeoff between quality of service (QoS) and operating expenditure (OPEX), the service provider should request the appropriate amount of resources to the cloud operator based on the estimated variation of service requests. This paper proposes a popularity-aware service provisioning framework (PASPF), which leverages the network data analytics function (NWDAF) to obtain analytics on service popularity variations. These analytics estimate the congestion level and list of top services contributing most of the traffic change. Based on the analytics, PASPF enables the service provider to request the appropriate amount of resources for each service for the next time period to the cloud operator. To minimize the OPEX of the service provider while keeping the average response time of the services below their requirements, we formulate a constrained Markov decision process (CMDP) problem. The optimal stochastic policy can be obtained by converting the CMDP model into a linear programming (LP) model. Evaluation results demonstrate that the PASPF can achieve less than 50% OPEX of the service provider compared to a popularity-non-aware scheme while keeping the average response time of the services below the requirement.

A state-space relational event modeling approach for learning dynamic social interaction behavior

Relational event models (REMs) are the primary choice for the analysis of relational-event network data. However, the standard REM assumes static parameters, which hinders the modeling of time-varying dynamics. This assumption might be too restrictive in real-life scenarios, making a model that allows for time-varying parameters more valuable. We introduce a state-space extension of the relational event model as a way to tackle this problem. The model has three main attributes. First, it provides a statistical framework of the temporal change of the parameters. Second, it enables the forecasting of future parameter values (which can be utilized to simulate new networks that can account for temporal dynamics in out-of-sample predictions). Third, it requires smaller data structures to be loaded into computer memory compared to the standard REM; this makes the model easily scalable to large networks. We conduct empirical analyses on bike-sharing data, corporate communications, and interactions among socio-political actors to illustrate model usage and applicability.

Retraction Note: Utilizing hybrid computing models for network monitoring and security analysis through optical network modeling and data analytics

Retraction Note: Utilizing hybrid computing models for network monitoring and security analysis through optical network modeling and data analytics

Network compression with configuration models and the minimum description length.

Random network models, constrained to reproduce specific statistical features, are often used to represent and analyze network data and their mathematical descriptions. Chief among them, the configuration model constrains random networks by their degree distribution and is foundational to many areas of network science. However, configuration models and their variants are often selected based on intuition or mathematical and computational simplicity rather than on statistical evidence. To evaluate the quality of a network representation, we need to consider both the amount of information required to specify a random network model and the probability of recovering the original data when using the model as a generative process. To this end, we calculate the approximate size of network ensembles generated by the popular configuration model and its generalizations, including versions accounting for degree correlations and centrality layers. We then apply the minimum description length principle as a model selection criterion over the resulting nested family of configuration models. Using a dataset of over 100 networks from various domains, we find that the classic configuration model is generally preferred on networks with an average degree above 10, while a layered configuration model constrained by a centrality metric offers the most compact representation of the majority of sparse networks.

Detection of cyberattacks using bidirectional generative adversarial network

Due to the progress of communication technologies, diverse information is transmitted in distributed systems via a network model. Concurrently, with the evolution of communication technologies, the attacks have broadened, raising concerns about the security of networks. For dealing with different attacks, the analysis of intrusion detection system (IDS) has been carried out. Conventional IDS rely on signatures and are time-consuming for updation, often lacking coverage for all kinds of attacks. Deep learning (DL), specifically generative methods demonstrate potential in detecting intrusions through network data analysis. This work presents a bidirectional generative adversarial network (BiGAN) for the detection of cyberattacks using the IoT23 database. This BiGAN model efficiently detected different attacks and the accuracy and F-score values achieved were 98.8% and 98.2% respectively.

GLAMLE: inference for multiview network data in the presence of latent variables, with an application to commodities trading

The statistical analysis of multiview network data provides researchers with insights on the mechanism governing many complex systems of interconnected elements. A graph is routinely applied to model this type of data: this poses several statistical challenges. Among them, there is the need for considering that some unobservable factors may determine the network connections and the graph topology. This requirement entails both complex models and computationally heavy inference procedures. To cope with these inference issues, a novel and flexible class of latent variable models labeled Graph Generalized Linear Latent Variable Model (GGLLVM) is introduced. Then, its parameters are estimated via the maximization of the Laplace-approximated likelihood. This leads to the creation of an M-estimator which is named Graph Laplace-Approximated Maximum Likelihood Estimator (GLAMLE). The statistical properties of this new estimator are characterized, and numerical experiments on simulated networks are performed to illustrate that the GLAMLE yields fast and accurate inference, in comparison to other widely used approaches. A real data application to commodities trading in European countries illustrates how the use of GGLLVM and GLAMLE unveils the import/export propensity that each node of the network has toward other nodes, along with additional information specific to each traded commodity.

Advanced modelling and recurrent analysis in network security: Scrutiny of data and fault resolution

This research dealt with the critical integration of advanced modelling techniques and recurrent analysis within network security, with a primary goal of enhancing the critical analysis of network data and improving fault resolution processes. The study focuses on the development of advance, predictive models capable of identifying and mitigating security threats in real-time, leveraging the power of Recurrent Neural Networks (RNNs) alongside other sophisticated machine learning techniques. By harnessing the dynamic capabilities of these models, the research aims to address the growing complexity and sophistication of network threats, which require continuous monitoring and adaptive responses. Also, the study investigates the effectiveness of these advanced models in environments where network conditions are constantly evolving, necessitating security protocols that can dynamically adjust to new and emerging threats. Through rigorous data scrutiny and recurrent analysis, the research seeks to establish fault resolution mechanisms that not only detect and neutralize immediate security breaches but also anticipate potential vulnerabilities before they can be exploited. Ultimately, this research contributes to the advancement of network security by providing a framework that integrates cutting-edge technology with real-time adaptability, ensuring that security measures remain robust and effective in the face of ever-changing digital threats.

Application Design and Analysis of Network Data Mining in Microsystemic Software

Application Design and Analysis of Network Data Mining in Microsystemic Software

Machine Vision Technology Based on Wireless Sensor Network Data Analysis for Monitoring Injury Prevention Data in Yoga Sports

Machine Vision Technology Based on Wireless Sensor Network Data Analysis for Monitoring Injury Prevention Data in Yoga Sports

NetDiffus: Network traffic generation by diffusion models through time-series imaging

While Machine-Learning based network data analytics are now commonplace for many networking solutions, nonetheless, limited access to appropriate networking data has been an enduring challenge for many networking problems. Causes for lack of such data include complexity of data gathering, commercial sensitivity, as well as privacy and regulatory constraints. To overcome these challenges, we present a Diffusion-Model (DM) based end-to-end framework, NetDiffus, for synthetic network traffic generation which is one of the emerging topics in networking and computing system. NetDiffus first converts one-dimensional time-series network traffic into two-dimensional images, and then synthesizes representative images for the original data. We demonstrate that NetDiffus outperforms the state-of-the-art traffic generation methods based on Generative Adversarial Networks (GANs) by providing 66.4% increase in the fidelity of the generated data and an 18.1% increase in downstream machine learning tasks. We evaluate NetDiffus on seven diverse traffic traces and show that utilizing synthetic data significantly improves several downstream ML tasks including traffic fingerprinting, anomaly detection and traffic classification. The code has been included at https://github.com/Nirhoshan/NetDiffus.

A trust distributed learning (D‐NWDAF) against poisoning and byzantine attacks in B5G networks

AbstractBeyond 5G networks, (B5G) introduce the emergence of a smart service and an automate management archetype of running network slices, such as the network data analytics function (NWDAF). These new architectures provide a large usage of advanced machine learning algorithms, in order to dynamically build efficient decisions. In this perspective, the Distributed Learning (D‐), known as A Distributed NWDAF Architecture (D‐NWDAF) has proved its efficiency in not only building collaborative deep learning models, among several network slices, but also ensuring the privacy and isolation of such network slices. Notwithstanding, FL is vulnerable to different attacks, where FL nodes (Leaf NWDAF: AI‐VNFs*) may upload malicious updates to the FL central (root NWDAF) entity so that it can cause a construction failure of the FL global model and affect the global performance. Moreover, attacks detection of FL‐based solutions give “black‐box” decisions about the performance of running network slices and the attacks detected. In other words, detection solutions do not provide any details about why and how such ML attacks decisions were made. Thus, such decisions cannot be properly trusted and comprehended by B5G slice managers. To resolve this issue, we leverage Dimensional Reduction (DR) and Explainable Artificial Intelligence (XAI) algorithms which aim to detect attacks and improve the transparency of black‐box FL attacks detection‐making process. In the present article, we design a novel DR‐XAI‐powered framework to detect attacks and explain the FL‐based attacks detection. We first build a deep learning model in a federated way, to predict key performance indicators (KPI) of network slices. Then, we try to detect the malicious FL nodes, using DR and several XAI models, such as RuleFit, in order to enhance the level of trustiness, transparency, and the explanation of the FL‐based attacks detection, while adhering the data privacy, to different B5G network stakeholders.

Research on rural tourism environment perception based on grounded theory A case study of Beishan Village, Zhuhai City, Guangdong Province, China

Drawing upon its regional culture, natural landscapes, and architectural features, Beishan Village in Zhuhai City, Guangdong Province, has forged a distinctive rural development model that combines an industrial park with tourism. While this approach has catalyzed rural progress, it also encounters numerous complex practical challenges. Utilizing the grounded theory method, this study employs participatory observation, in-depth interviews, and network data analysis to investigate the perspectives of three key stakeholder groups: villagers, tourists, and resident merchants. A model is constructed to capture their environmental perceptions of rural tourism. Based on an analysis of network and interview textual data, as well as the influencing factors identified through the theoretical model, this study proposes several optimization strategies. These include enhancing infrastructure development, cultivating a regional brand culture, strengthening the institutional management framework, establishing a rural sharing economy platform, and introducing digital tours. These strategies are designed to refine and enhance Beishan Village's development model, offering insights for similar villages and advancing the integration of beautiful China initiatives with urban and rural development efforts.

Variational inference for the latent shrinkage position model

AbstractThe latent position model (LPM) is a popular method used in network data analysis where nodes are assumed to be positioned in a ‐dimensional latent space. The latent shrinkage position model (LSPM) is an extension of the LPM which automatically determines the number of effective dimensions of the latent space via a Bayesian nonparametric shrinkage prior. However, the LSPM's reliance on Markov chain Monte Carlo for inference, while rigorous, is computationally expensive, making it challenging to scale to networks with large numbers of nodes. We introduce a variational inference approach for the LSPM, aiming to reduce computational demands while retaining the model's ability to intrinsically determine the number of effective latent dimensions. The performance of the variational LSPM is illustrated through simulation studies and its application to real‐world network data. To promote wider adoption and ease of implementation, we also provide open‐source code.

Consumer Behavior Prediction and Market Application Exploration Based on Social Network Data Analysis

It examines the junction of consumer behavior prediction and market application exploration using social network data analysis. Using the massive quantity of information available on social media sites, they use complex data mining tools to estimate consumer opinions, preferences, and behaviours. They discover useful insights that inform strategic decision-making processes for enterprises across several industry domains by combining approaches such as predictive modelling, natural language processing, and social network analysis. Results show that social network data analysis can effectively anticipate consumer actions and detect market trends. They employ sentiment analysis to categorize user sentiments regarding products, companies, and marketing campaigns, delivering actionable insights for marketing plan optimization. Furthermore, predictive modelling helps us to estimate purchase intent, segment clients, and spot new trends, allowing businesses to customize their products and improve customer engagement. Additionally, this study emphasizes the significance of addressing ethical concerns and privacy consequences in social network data analysis. Businesses that embrace transparent and responsible data policies can create consumer trust while also mitigating the dangers related to data misuse.

Exploring and Analyzing Network Data with Python

Cette leçon présente différents indicateurs de réseau et la manière de les interpréter lorsque l'on travaille avec des données relationnelles en sciences humaines et sociales. Vous apprendrez à utiliser la bibliothèque Python NetworkX pour calculer et interpréter ces indicateurs.

Hierarchical Bayesian adaptive lasso methods on exponential random graph models

The analysis of network data has become an increasingly prominent and demanding field across multiple research fields including data science, health, and social sciences, requiring the development of robust models and efficient computational methods. One well-established and widely employed modeling approach for network data is the Exponential Random Graph Model (ERGM). Despite its popularity, there is a recognized necessity for further advancements to enhance its flexibility and variable selection capabilities. To address this need, we propose a novel hierarchical Bayesian adaptive lasso model (BALERGM), which builds upon the foundations of the ERGM. The BALERGM leverages the strengths of the ERGM and incorporates the flexible adaptive lasso technique, thereby facilitating effective variable selection and tackling the inherent challenges posed by high-dimensional network data. The model improvements have been assessed through the analysis of simulated data, as well as two authentic datasets. These datasets encompassed friendship networks and a respondent-driven sampling dataset on active and healthy lifestyle awareness programs.