Search

The needs of modern businesses, along with advancements in digital technologies, are forcing a more decentralized digital operating model on many of today's companies. The monolithic applications of the past [...]

Enterprise networks that host valuable assets and services are popular and frequent targets of distributed network attacks. In order to cope with the ever-increasing threats, industrial and research communities develop systems and methods to monitor the behaviors of their assets and protect them from critical attacks. In this survey, we systematically survey related research articles and industrial systems to highlight the current status of this arms race in enterprise network security. First, we discuss the taxonomy of distributed network attacks on enterprise assets, including reconnaissance attacks probing vulnerable enterprise hosts and servers, and distributed denial-of-service (DDoS) attacks aiming to paralyze network services hosted within an enterprise network. Second, we review existing methods that leverage either static configurations or dynamic network graphs to monitor network behavior of enterprise hosts, verify their benign activities and isolate potential anomalies. Third, state-of-the-art detection methods for distributed network attacks sourced from external attackers, such as proprietary rules in commercial firewalls and community signatures in open-source software tools are elaborated with highlights on their merits and bottlenecks. Fourth, as programmable networks and machine learning (ML) techniques are increasingly becoming adopted by the community, their current applications in network security are discussed. Finally, we suggest several research gaps on enterprise network security to inspire future research.

In this work, we enhance the sustainability of an enterprise network (EN) by complementing it with an expert system that apprehends the explore‐exploit behavioural intelligence of Physarum to survive against the attractive‐adversarial nutritional environment. EN sustainability is dynamic since it depends on how well EN can react to an adversarial environment. We capture a reverse analogy to characterize EN's workload‐environment with Physarum's nutritive‐environment, where the high volume of workloads at the backbone network corresponds to a poor‐nutrient environment. The expert system explores EN to find out how to manage the workloads as Physarum handles its survivability, and exploits the users' workload patterns by grouping the highly communicating users together to redesign the network structure as Physarum's intelligence to exploit energy from rich‐ and poor‐nutrient food sources through redesigned tubular structures. We define two factors, such as nutrient‐intensity and chemo‐attractant to aid the redesign process. EN evolves through a set of redesigned clusters with an objective function to maximize its sustainability for a given set of explored workloads by minimizing the workloads through the backbone. EN evolution terminates when there is no change in the backbone utilization, resembling the organism's stay in a dormant state until it experiences a favourable environment. Our experimental results on an EN with a higher volume of workloads at the backbone producing 14.26 kWh energy consumption demonstrated that the developed expert system reduced the energy consumption to 11.27 kWh, thus enhanced the sustainability from 21% to 61%. [ABSTRACT FROM AUTHOR]

Multi-agent cooperation, pooling innovative resources, helps to develop key technologies and major projects in enterprises. However, the multi-agent cooperation of enterprises makes the cooperative relationship unstable, and behaviours such as moral hazard, spillover of technology or information are more likely to occur, thereby increasing the risk of enterprises. In this research an enterprise R&D cooperative evolutionary game model is built to investigate the influence of Network externalities on enterprise R&D cooperation in innovation networks. Furthermore, the evolution of innovative network structure of game results is simulated by means of computer technology so that the effects of network externalities on corporation degree, agglomeration and network structure are analyzed. The results show that Network externalities led to a decrease of cooperated partners, a decline in cooperation degree and agglomeration of enterprises, and a shrinking enterprise R&D cooperation tendency. The influence of network externalities shows significant structural differences in the three types of enterprise networks: the degree of enterprise cooperation and agglomeration fluctuates greatly in the scale-free network, but slightly in the small-world network and the random network. [ABSTRACT FROM AUTHOR]

Against the backdrop of digital technology integration and digital transformation, the digital economy has become an important new economic development in China. The digital economy changes and subverts the traditional production and organization modes of industries, breaks regional and spatial barriers, and realizes a highly interconnected network structure. Therefore, it is critical to study the digital economy from a network analysis perspective. In previous studies, the interlocking network model and the headquarters-branch model have been adopted to construct enterprise networks; however, the networks based on these models have some deficiencies in comprehensiveness and authenticity. An enterprise equity network built based on the investment relationships between enterprises can reflect the actual contact network status of the enterprise. Based on the above background, this study analyzed the evolutionary characteristics of the digital economy network in the Yangtze River Delta area through an enterprise equity network and explored its influencing factors and mechanisms. First, we constructed the digital economy networks of the Yangtze River Delta area for 2010, 2015, and 2020 using equity investment data between enterprises and a directed correlation network model. Second, with the indicators of degree centrality, degree centralization, an investment source, and others, the spatial and temporal evolution characteristics of digital economy networks were described from two aspects: network patterns and investment relationships. Furthermore, this study explored the possible factors affecting digital economy networks in the Yangtze River Delta area through a Quadratic Assignment Procedure. This study has three main findings: 1) From 2010 to 2020, various types of digital economy networks in the Yangtze River Delta area showed the characteristics of hierarchical diffusion and node agglomeration. Each industry forms an expansion of the network framework around the core node in the initial stage and then expands to the surrounding areas through secondary nodes. Shanghai and Hangzhou were the most important network nodes in the digital economy. 2) The number of net inflow areas for digital economy investment in the Yangtze River Delta was increasing, whereas the number of net outflow areas was decreasing. Hangzhou was the main net inflow city, while Shanghai and Ningbo were the main net outflow cities. Simultaneously, the source of investment in research units has changed from inside the province to outside, and there are clear interprovincial differences. 3) Cognitive proximity, the difference in the number of digital economy enterprises, the difference in the proportion of secondary industries, the difference in information facilities, and the early network foundation all have significant positive effects on the establishment of digital economy networks. System proximity, the difference in the proportion of tertiary industries, the time difference in the establishment of digital economy enterprises, and the gap in innovation ability restrict the formation and development of digital economy networks. Geographical distance is no longer the main factor influencing the formation of digital economic networks. Based on the above conclusions, this study proposes some suggestions for promoting the development of the digital economy from the perspectives of industrial agglomeration, facility and policy allocation, and path dependence.

Zero Trust (ZT) is security model and follow the concept of ânever trust, always verifyâ. ZT require to strict identity verification for devise and clients trying to access recourses on private networks regardless of whether they are sitting within or outside the networks. As opposed to perimeter-based architecture, which makes the assumption that all internal network parties are trusted and all external network parties are untrusted. In enterprise network the internal network parties is automatically seen as trusted entities granting them access to network resources. The insider threat actor has been successful in exploiting their access. So that, enterprise networks become more exposed to inside and outside threads. As a result, we need to add the zero-trust principle to the enterprise network to protect it from the inside. In this paper, the ZT model, is assumed inside the militarized zone. There may be a threat to the sensitive data. Any internal company network users cannot manipulation on his computer without permission from the administrator this is done by the group policies that have been implemented in ZT. This model has been shown to be quite effective in protecting the sensitive data against unauthorized access and also the manipulation by the insider user. Following that, an attack originating from inside of the network was launched against the enterprise and zero trust network. In the enterprise network, the network was effectively attacked, and the attack’s validity was also increased to fully penetrate the enterprise. The attack did not succeed in the zero-trust network because the attacker cannot pass the User Account Control (UAC) to gain the NT authority.

Carbon emissions, as an indicator of green economic development in urban agglomerations, are closely related to the degree of coordinated development between cities. Additionally, urban agglomerations, as a highly developed form of urban space, are widely regarded as a more efficient, energy-saving, and land-saving urbanization method. This article constructs an urban agglomeration network based on relevant data from listed companies in the Yangtze River Delta urban agglomeration with practical connections between cities and uses social network analysis methods and a fixed effects model to calculate the impact of overall and individual network indicators of urban agglomerations on urban carbon emissions and collaborative emission reduction of urban agglomerations. The regression results indicate that the centrality of individual cities has a significant negative correlation with the intensity of urban carbon emissions, with a coefficient of −0.067. The centrality of core cities has a significant positive impact on the collaborative emission reduction of urban agglomerations, with a coefficient of 0.0138. The impact of network density on the collaborative emission reduction of urban agglomerations shows an inverted U-shaped curve. Based on the analysis results, the paper explores the spatial structure construction method and industrial development control strategy based on urban agglomeration collaborative emission reduction. [ABSTRACT FROM AUTHOR]

Against the backdrop of digital technology integration and digital transformation, the digital economy has become an important new economic development in China. The digital economy changes and subverts the traditional production and organization modes of industries, breaks regional and spatial barriers, and realizes a highly interconnected network structure. Therefore, it is critical to study the digital economy from a network analysis perspective. In previous studies, the interlocking network model and the headquarters-branch model have been adopted to construct enterprise networks; however, the networks based on these models have some deficiencies in comprehensiveness and authenticity. An enterprise equity network built based on the investment relationships between enterprises can reflect the actual contact network status of the enterprise. Based on the above background, this study analyzed the evolutionary characteristics of the digital economy network in the Yangtze River Delta area through an enterprise equity network and explored its influencing factors and mechanisms. First, we constructed the digital economy networks of the Yangtze River Delta area for 2010, 2015, and 2020 using equity investment data between enterprises and a directed correlation network model. Second, with the indicators of degree centrality, degree centralization, an investment source, and others, the spatial and temporal evolution characteristics of digital economy networks were described from two aspects: network patterns and investment relationships. Furthermore, this study explored the possible factors affecting digital economy networks in the Yangtze River Delta area through a Quadratic Assignment Procedure. This study has three main findings: 1) From 2010 to 2020, various types of digital economy networks in the Yangtze River Delta area showed the characteristics of hierarchical diffusion and node agglomeration. Each industry forms an expansion of the network framework around the core node in the initial stage and then expands to the surrounding areas through secondary nodes. Shanghai and Hangzhou were the most important network nodes in the digital economy. 2) The number of net inflow areas for digital economy investment in the Yangtze River Delta was increasing, whereas the number of net outflow areas was decreasing. Hangzhou was the main net inflow city, while Shanghai and Ningbo were the main net outflow cities. Simultaneously, the source of investment in research units has changed from inside the province to outside, and there are clear interprovincial differences. 3) Cognitive proximity, the difference in the number of digital economy enterprises, the difference in the proportion of secondary industries, the difference in information facilities, and the early network foundation all have significant positive effects on the establishment of digital economy networks. System proximity, the difference in the proportion of tertiary industries, the time difference in the establishment of digital economy enterprises, and the gap in innovation ability restrict the formation and development of digital economy networks. Geographical distance is no longer the main factor influencing the formation of digital economic networks. Based on the above conclusions, this study proposes some suggestions for promoting the development of the digital economy from the perspectives of industrial agglomeration, facility and policy allocation, and path dependence. [ABSTRACT FROM AUTHOR]

Enterprise Network (EN) has increasingly gained popularity in academia. Over the past few decades, a substantial amount of EN studies have been published in China. Drawing upon a sample of 603 papers retrieved from the Chinese Social Sciences Citation Index database (CSSCI) between 1998 and 2020, this study aims to delve into the status quo, knowledge base, research focus, and evolutionary trends of EN research in China. A multifaceted bibliometric analysis was performed using CiteSpace. The findings mainly indicate that the research on EN in China has a clear development context, and the research content gradually changes from macro to micro. In addition to foreign Social Network theories, the research results of domestic scholars have become the basic knowledge in this field. The research includes these topics: the conceptualization of EN, EN as indicators of enterprise development, EN's impact on start-ups, mechanisms of EN's effect and governance of EN. The potential direction for future research has been identified as the integration between EN and other forms of networks, and the structure of EN. [ABSTRACT FROM AUTHOR]

The importance of using and processing data in corporate networks is increasing day by day in the developing global world. Due to the nature of the systems, the use of data increases the network traffic density from the connected roads. The increase in data density is one of the most important factors affecting system infrastructures and performance values. Measuring and evaluating the effect of data usage amounts before the start of COVID-19 on the usage density rates of network traffic in corporate networks and the effect of network traffic density that develops with the onset of the pandemic process provides a better understanding of this structure. By using Wavelet Transform and Continuous Wavelet Transform, the temporal variation of data density was analyzed with an application based on the data set obtained from the system. The graphical data created after the analysis of the signal data (Mbps or Gbps) processed before and after the COVID-19 by using Wavelet Transform were examined and interpreted. a specific results, statistical magnitudes and different scale effects were analized. While there were 8791 data before COVID-19, 16167 data were taken into account after COVID-19. In addition, when the network traffic was examined after the pandemic period, it increased %141 times, higher thans the speed recorded before pandemic period similarly the average network traffic speed is %79 times. [ABSTRACT FROM AUTHOR]

To satisfy enterprise demands of analyzing and dealing with the large scale of data with lower costs, an effective method is to integrate the servers and computers and use virtualization technology to construct an enterprise network. Prior studies on network virtualization have mainly been executed in the cloud; however, these studies may not be appropriate for enterprise networks for two reasons: i) the goal of most of them is to generate more revenues for cloud providers, but focus less on saving costs; ii) the physical machines are relatively concentrated in the cloud platform but dispersed over different geographic locations in enterprise networks. In this paper, we solve the problem of energy-optimized virtual network embedding with location constraints (EO-VNE). First, the node and link capabilities in enterprise networks are defined in the form of complex number theory, unifying computers and virtual requests. Second, the normalized method of computing and storage capabilities are proposed to identify the node capability. Third, an energy model of the enterprise network is built, and using this model, EO-VNE is shown to be NP-complete. Finally, an energy-optimized virtual network embedding with a location constraint algorithm (EOLC) is proposed to minimize the energy consumption under the constraint of node position. The experiments show that EOLC consumes less energy compared with the algorithm of energy-aware virtual network embedding with dynamic demands (EAD). It also has better performance than the location constraint algorithm based on bisection (GLC).

Enterprise evaluation provides indicators such as ratings and scores by analyzing the characteristics and capabilities of enterprises. The business performance, the level of credit risk, and the economic value of technology are quantitatively evaluated. Although the existing methods are well established, they need improvement in three aspects: fragmentation of information, interpretability of results, and objectivity of evaluation. First, existing methods selectively utilizes the information according to its own purpose. Second, it is hard for those results to understand the rationale of evaluation and the characteristics of enterprise. Third, unofficial information such as personal opinions or profit structures are included in the evaluation. Motivated by the limitations, we propose a machine learning-based enterprise evaluation method consisting of diversified quantification and semi-supervised learning. By quantifying various information, the analysis for identifying enterprise characteristics is primarily performed, and the results are derived as several remarkable features to improve interpretability. Then, by constructing the network, enterprises have compared each other, and they are objectively evaluated by label propagation on the enterprise network. The output is measured as a score, and later its distribution is binned into five grades to improve practicality and usefulness. The proposed method was applied to the dataset of 27,790 enterprises with 113 variables about financial and R&D information. The results show clear identification of enterprise characteristics with the high accuracy of evaluation. [ABSTRACT FROM AUTHOR]

Objective: The study examined the mediating factors influencing the capacities and performance of enterprise network using some food and beverages enterprises in Lagos, Nigeria. Research Design & Methods: Primary source of data was employed in the study. The data collected from five hundred and thirty nine (539) respondents was analysed with the use of factor analysis which brings out the beauty and reality of the study. Six hundred and fifty seven (657) middle and top level management staff of six food enterprises and beverage enterprises particularly the manufacturing ones through a purposive sampling technique. Findings: When the original ten variables were analyzed, four variables were extracted from the analysis with eigenvalues greater or equal to 1, which explained 36.414 percent of the entire variance. Hence, the mediating factor influencing the capacities of enterprise network performance is Strategic Decision-Making Capacity (SDMC), Technological Capacity (TC), Efficiency Enterprise Capacity (EEC), and Complexity in Technology or Technological Turbulence (TT). Contribution & Value Added: Finally, factors influencing the capacities of network performance are Strategic Decision Making Capacity (SDMC), Technological Capacity (TC), Efficiency Enterprise Capacity (EEC), and Complexity in Technology or Technological Turbulence (TT). The result of the study is essential because of its significant contribution to the body of knowledge and literature regarding strategic management.

Objective: This study elucidates the empirical review of enterprise networks on company performance. Research Design & Methods: Enterprise networks are also referred to as corporate network which is among the major pillars of any business. The importance of enterprise networks cannot be far-fetched from or beyond its evolving supports through encapsulation of software applications, databases, for the purpose of achieving fully business goals. Findings: The empirical covers the essence of enterprise networks, reasons for enterprise networking in company performance, formation of enterprise network, the management of enterprise network, and the interplay between enterprise network and company performance. It was concluded in the previous studies that performance of enterprise networks could entail enterprise network survival, profit maximization, sales maximization, return on investment, sales growth and a number of employees. Contribution & Value Added: Hence, it is affirmed in this study that enterprise network performance is a set of financial and nonfinancial indices which offer information on the extent of achieving aim, objectives and better outputs of the enterprise network.

Network security, as the prerequisite for the normal operation of enterprise network, should not focus on a single point, but all aspects of the network, ranging from physics, network, system, application to management. To ensure enterprise network security and prevent network attacks, it is of great importance to build an intrusion detection system (IDS) capable of protecting the network and computers from malicious attacks based on the Internet or host. In light of the above, this paper puts forward an intrusion detection method for enterprise network based on backpropagation neural network (BPNN), and carries out Python simulation of the proposed method on four problems, namely, normal state, the SYN flood (denial-of-service attack), snoop (unauthorized access from a remote host), and saint (reconnaissance attack). The simulation results show that the BPNN-based method could effectively check the network security environment, and accurately identify and detect intrusions. [ABSTRACT FROM AUTHOR]

With increasing demands for more flexible services, the routing policies in enterprise networks become much richer. This has placed a heavy burden to the current router forwarding plane in support of the increasing number of policies, primarily due to the limited capacity in TCAM, which further hinders the development of new network services and applications. The scalable forwarding table structures for enterprise networks have therefore attracted numerous attentions from both academia and industry. To tackle this challenge, in this paper we present the design and implementation of a new forwarding table structure. It separates the functions of TCAM and SRAM, and maximally utilizes the large and flexible SRAM. A set of schemes are progressively designed, to compress storage of forwarding rules, and maintain correctness and achieve line-card speeds of packet forwarding. We further design an incremental update algorithm that allows less access to memory. The proposed scheme is validated and evaluated through a realistic implementation on a commercial router using real datasets. Our proposal can be easily implemented in the existing devices. The evaluation results show that the performance of forwarding tables under the proposed scheme is promising. [ABSTRACT FROM AUTHOR]

The collaboration within organisations and among organisations is a fundamental concept in the attainment of the overall objectives pursued by an enterprise network in human companies. Swarm systems are intelligent systems that show collaboration within the system; moreover, some models, such as multiple ant colonies, show the collaboration of several systems to achieve a global goal. The collaboration in this type of system optimises the achievement of the overall objectives as in an enterprise network in human organisations. Being able to measure this collaboration allows establishing a relationship between the improvement in the results of the system and the degree of collaboration, both at the level of specialisation of each element of the system and the systems as a whole. The performance of a swarm system depends on the number of members in many cases, so that if we can establish a measure of specialisation and collaboration, we could tipify and classify these systems in terms of the efficiency and the realiability to perform different tasks. [ABSTRACT FROM AUTHOR]

A lot of network management tasks depend on the description of the network topology. However, the lack of standard methods of the network elements detection, coupled with the incompleteness and heterogeneity of the available topology data, complicate the network topology discovery process. In these conditions, formal models and methods of topology discovery are required. Contribution of the paper is an algorithm for automated enterprise network topology discovery based on a previously developed graph model and criteria for building graph elements. The proposed algorithm is capable of dealing with incomplete heterogeneous data about network topology as well as with the presence in the network of uncooperative devices. The paper also evaluates the algorithm and provides the testing results.

Concept selection in new product development projects are a crucial task that need to be carried out effectively to maximize enterprise's profitability. A risk based design concept selection methodology was evolved by Goswami et al.(2014). In the context of this work, we aim to analyze the implications of this research by: a) Comparing and validating the proposed methodology as devised by relevant existing measure(s) in current research literature and propose various improvement ideas that can either change the selection decisions or further improve them b) Ignoring the interaction among the root nodes, evaluate whether selection decisions pertaining to various module instances is affected; c) Assess whether selection decisions change across with respect to the size of the firm i.e. local vs global OEM. [ABSTRACT FROM AUTHOR]