Networked Virtual Environments Research Articles

Anomaly detection based on system text logs of virtual network functions

In virtual network environments building secure and effective systems is crucial for its correct functioning, and so the anomaly detection task is at its core. To uncover and predict abnormalities in the behavior of a virtual machine, it is desirable to extract relevant information from system text logs. The main issue is that text is unstructured and symbolic data, and also very expensive to process. However, recent advances in deep learning have shown remarkable capabilities of handling such data. In this work, we propose using a simple LSTM recurrent network on top of a pre-trained Sentence-BERT, which encodes the system logs into fixed-length vectors. We trained the model in an unsupervised fashion to learn the likelihood of the represented sequences of logs. This way, the model can trigger a warning with an accuracy of 81% when a virtual machine generates an abnormal sequence. Our model approach is not only easy to train and computationally cheap, it also generalizes to the content of any input.

Performance evaluation of containers for low-latency packet processing in virtualized network environments

Packet processing in current network scenarios faces complex challenges due to the increasing prevalence of requirements such as low latency, high reliability, and resource sharing. Virtualization is a potential solution to mitigate these challenges by enabling resource sharing and on-demand provisioning; however, ensuring high reliability and ultra-low latency remains a key challenge. Since bare-metal systems are often impractical because of high cost and space usage, and the overhead of virtual machines (VMs) is substantial, we evaluate the utilization of containers as a potential lightweight solution for low-latency packet processing. Herein, we discuss the benefits and drawbacks and encourage container environments in low-latency packet processing when the degree of isolation of customer data is adequate and bare metal systems are unaffordable. Our results demonstrate that containers exhibit similar latency performance with more predictable tail-latency behavior than bare metal packet processing. Moreover, deciding which mainboard architecture to use, especially the cache division, is equally vital as containers are prone to higher latencies on more shared caches between cores especially when other optimizations cannot be used. We show that this has a higher impact on latencies within containers than on bare metal or VMs, resulting in the selection of hardware architectures following optimizations as a critical challenge. Furthermore, the results reveal that the virtualization overhead does not impact tail latencies.

VIRTUAL NETWORK ENVIRONMENT AS A SPACE FOR FORMING DIALOGUE BETWEEN AUTHORITY AND SOCIETY

The modern virtual network environment opens up new opportunities for involving the public in government processes and ensuring greater influence of citizens on decision-making; The online environment increases the level of openness and transparency, promotes interaction between government and the public, thereby strengthening the democratic principles of society. In the era of global digitalization, it is thanks to the virtual network environment that it is possible to form a high-quality and effective government- public dialogue, which is a key aspect of civil participation in government. The article examines the virtual network environment as a platform for the formation of high-quality and effective dialogue between the government and society in order to solve pressing problems and support democratic values. In particular, attention is focused on: key positions of the virtual network environment and its characteristics; principles of government-public dialogue; factors influencing the success and effectiveness of government-public dialogue through a virtual network environment; challenges and opportunities that arise when implementing a virtual network environment as a platform for government-public dialogue.

Slice admission control in 5G cloud radio access network using deep reinforcement learning: A survey

SummaryThe emergence of 5G networks has increased the demand for network resources, making efficient resource management crucial. Slice admission control (SAC) is a process that governs the creation and allocation of virtualized network environments, known as “network slices,” which can be tailored to meet specific user requirements. However, traditional SAC methods face dynamic and heterogeneous challenges in wireless networks, especially in cloud radio access networks (C‐RANs). To address this issue, machine learning (ML) techniques, particularly deep reinforcement learning (DRL), have been proposed as powerful tools for optimizing SAC. DRL‐based approaches enable SAC systems to learn from previous interactions with the network environment and dynamically adapt to changing network conditions. This review article comprehensively explains the current state‐of‐the‐art DRL‐based SAC, focusing on C‐RANs. The article identifies key challenges and future research directions and highlights the potential benefits of using DRL for SAC, including improved network performance and efficiency. However, deploying these systems in real‐world scenarios presents several challenges and trade‐offs that need to be carefully considered. Further research and development are required to address these challenges and ensure the successful deployment of DRL‐based SAC systems in wireless networks.

Research and Application of Active Security Protection Methods for Automotive Cloud

With the gradual maturity of the Internet of vehicles and cloud computing industry and the continuous evolution of network attack forms, the demand for cloud security in the automotive industry will increase year by year. In the automobile cloud platform environment, because the traditional security scheme is still effective for the north-south traffic, the security problem of the automobile cloud platform mainly lies in the security protection of the east-west traffic of the platform and the division of the security boundary. The purpose of this project is to solve the security risk of east-west cross-subnet traffic of the automobile cloud platform, especially the traffic security problem between hosts, in order to provide the next generation firewall, intrusion prevention and other professional security protection functions for the virtual network environment without affecting the service virtual machine. In order to achieve the above purpose, it is necessary to solve the problem of virtual switch diversion and virtual machine drift on the cloud platform, study the service security requirements of the automotive cloud platform, the relationship between data transfer and processing on the cloud platform, and finally provide a prototype system of active security protection for the cloud platform risks. The system implements VM microisolation, network attack defense, malicious code defense, ip address-based secure access policies, application-type secure access policies, and VM migration security functions in cloud application scenarios.

Design of 3D Environment Combining Digital Image Processing Technology and Convolutional Neural Network

As virtual reality technology advances, 3D environment design and modeling have garnered increasing attention. Applications in networked virtual environments span urban planning, industrial design, and manufacturing, among other fields. However, existing 3D modeling methods exhibit high reconstruction error precision, limiting their practicality in many domains, particularly environmental design. To enhance 3D reconstruction accuracy, this study proposes a digital image processing technology that combines binocular camera calibration, stereo correction, and a convolutional neural network (CNN) algorithm for optimization and improvement. By employing the refined stereo-matching algorithm, a 3D reconstruction model was developed to augment 3D environment design and reconstruction accuracy while optimizing the 3D reconstruction effect. An experiment using the ShapeNet dataset demonstrated that the evaluation indices—Chamfer distance (CD), Earth mover’s distance (EMD), and intersection over union—of the model constructed in this study outperformed those of alternative methods. After incorporating the CNN module in the ablation experiment, CD and EMD increased by an average of 0.1 and 0.06, respectively. This validates that the proposed CNN module effectively enhances point cloud reconstruction accuracy. Upon adding the CNN module, the CD index and EMD index in the dataset increased by an average of 0.34 and 0.54, respectively. These results indicate that the proposed CNN module exhibits strong predictive capabilities for point cloud coordinates. Furthermore, the model demonstrates good generalization performance.

Study on the differences in behaviour characteristics of distance online autonomous learning in different network virtual environments

Study on the differences in behaviour characteristics of distance online autonomous learning in different network virtual environments

Equilibrium of individual concern-critical influence maximization in virtual and real blending network

Recently, the Metaverse has received much attention, especially from large companies, such as Meta. The social nature of the Metaverse opens new ways for immersive social interactions that combine the strengths of physical and virtual worlds. This scenario creates new possibilities for social interactions that can worsen problems like the Echo chamber or the information cocoon effects. In this work, we devise a virtual and real blending network environment and introduce the concern vector indicator to explore the equilibrium of individual concern-critical influence maximization (EIC-cIM). We compute the complexity of the EIC-cIM and verify the modularity of the objective function. A surrogate objective function is introduced to tackle the problem of the EIC-cIM. We exploit the continuous relaxation technique to develop an approximate projected subgradient algorithm, which effectively guarantees the balance of individual concern in the information diffusion process. The experimental simulations on three network data sets demonstrate the effectiveness of our method, and the experimental results show that our proposed algorithm outperforms the existing heuristic algorithms by at least 23% in performance and even outperforms the max-greedy algorithm in some cases.

Key Technologies for Networked Virtual Environments

Thanks to the improvements experienced in technology during the last few years, most especially in virtual reality systems, the number and potential of networked virtual environments or NVEs and their users have been increasing. NVEs aim to give distributed users a feeling of immersion in a virtual world and the possibility of interacting with other users or with virtual objects inside it, just like when they interact in the real world. Being able to provide that feeling and natural interactions when the users are geographically separated is one of the goals of these systems. Nevertheless, this goal is especially sensitive to different issues, such as different connections with heterogeneous throughput or particular network latencies, which can lead to consistency and synchronization problems and, thus, to a worsening of the users’ quality of experience or QoE. With the purpose of solving these issues, researchers have proposed and evaluated numerous technical solutions, in fields like network architectures, data distribution and filtering, resource balancing, computing models, predictive modeling and synchronization in NVEs. This paper gathers and classifies them, summarizing their advantages and disadvantages, using a new way of classification. With the current increase in the number of NVEs and the multiple solutions proposed so far, this paper aims to become a useful tool and a starting point not only for future researchers in this field but also for those who are new to NVEs development, in which guaranteeing a good users’ QoE is essential.

The Influence of Internet Violence on The Mental Health of College Students in The New-media Era

The advent of the era of "new media" makes the online social environment more open. College students, as one the mainstream users of online social media, will inevitably have conflicts when communicating with the virtual network environment, and the phenomenon of online violence happens from time to time, which has a great impact on the mental health of college students. This study selected the college students to participate in the network violence groups as the investigation object, the construction of college students to participate in the theoretical model of network violence, and then put forward the research hypothesis, and design the questionnaire. Finally, it was concluded that the occurrence of network violence will cause psychological harm to the participants or victims of college students, and although college students do not approve of network violence, they still have deficiencies in behavior control, and they still use network violence to achieve their own goals. The results of this study had reference significance for the psychological rehabilitation of college students after the occurrence of cyber violence.

Intrapersonal Communication in the Perspective of WeChat’s Circle of Friends Self-presentation Analysis

In the mobile Internet era, social media as a medium based on virtual platforms has dramatically changed the form of people's social interaction. the Circle of Friends, as an essential social module in the most used instant messaging software, carries the function of users' self-presentation and communication and creates an online social platform for WeChat users that is not only in line with the actual social relations but also relies on the virtual platform. The expression of the "self" relies on the regulation of the "guest me" to the real reflection of the "master me", and the online socialization platform provided by WeChat's Friend Circle encourages users to express their own emotions, and the same time, it also encourages users to express their emotions. The online social platform provided by the WeChat Friendship Circle encourages users to express their own emotions and provides users with social feedback with personal cognitive significance due to its social interaction method that is both dependent on the virtual network environment and rooted in real social relationships. Through the comparison of different sharing contents and personal expression forms, users can obtain more accurate psychological expectations of social feedback and a form of self-presentation that is more in line with their personal psychological needs, to realize their needs for a good social experience and the regulation and development of self-expression within the social media platform. This paper takes the social media context provided by WeChat's Circle of Friends as the entry point, starts from the perspective of intra-personal communication, disassembles the process of self-perception and presentation, the two comparative cognitive methods of "vertical" and "horizontal" are used to categorize the process of WeChat users' self-recognition on the platform of the Circle of Friends and the regulation of self-expression in the online environment, and to discuss the situation of users' self-representation in the online social environment of the Circle of Friends.

Study on the differences in behaviour characteristics of distance online autonomous learning in different network virtual environments

Study on the differences in behaviour characteristics of distance online autonomous learning in different network virtual environments

Security Evaluation of Situational Awareness in Cyberspace Based on Artificial Neural Network-Back Propagation

Computer network security has become increasingly controversial among many businesses as a result of the rise in cyber threats. Artificial neural network (ANN) is mature research in this field, whereas the traditional algorithm is slower, in feedback error, and has the disadvantage of easy convergence to local extreme value. To guide against these threats, in this paper, ANN-back propagation (BP) algorithm is used to establish the relationship between the level of cyber security situational awareness (CSA) and the perceptual parameters and quantitatively evaluate the situational awareness. This study established the ANN-back propagation (BP) to make the relationship between the level of cyber security situational awareness (CSA) and the perceptual parameters, which evaluates situational awareness. The ANN-BP with variable step size learning strategy and simulated annealing method is used for optimization to build a virtual network environment. The proposed model offers better precision, improved sensitivity, and higher (0.987%) accuracy.

Game theory-based dynamic resource allocations scheme in virtual networks

ABSTRACT This paper deals with resource allocation challenge focusing on Infrastructure Providers (InPs) and Service Providers (SPs) in virtual network environments. In such environments, resources (storage space, bandwidth, CPU, …) can be reallocated easily between users, involving Cloud services widespread. However, while these resources come from physical network, the allocations are constrained. To maintain on demand resources provisioning, service providers must frequently deal with many parameters (use duration, price, availability, fidelity, …) which involve unfair and non-optimal allocation scheme. We propose two game theory-based solutions that allow physical infrastructure providers to assign resources to service providers. Whereas literature works consider only one type of resource, we consider several types, such as storage space, bandwidth, RAM and processor. We design a set of non-cooperative games between physical infrastructure providers and service providers. Simulation results show that our solutions optimize resource usage and service quality compared to the literature.

Application of an Innovative Convolutional/LSTM Neural Network for Computing Resource Allocation in NFV Network Architectures

An innovative resource allocation framework for virtualized network environments and based on the application of Artificial Intelligence techniques is proposed and investigated. It integrates the needed processing capacity prediction procedure and the allocation one which determines the capacity under-allocation or over-allocation needed to minimize the resource allocation and Quality of Service degradation costs. The proposed solution is based on: i) a monitoring procedure in which the processing capacities required by virtual instances are periodically monitored; ii) an integrated allocation/prediction procedure in which the processing capacities to be allocated to the virtual instances are evaluated in time intervals successive to the monitoring periods. This second procedure uses a Convolutional/Long Short Term Memory neural network whose loss function is defined so as to minimize an overall cost dependent on both the cloud resource allocation and Quality of Service degradation costs. We evaluate the proposed solution in backbone and metropolitan traffic and network scenario. We show how in the traffic scenario of an Italian Mobile Operator in Milan zone, the proposed solution far outperforms the not integrated classical solution in which the capacity prediction and allocation procedures are separately performed. Furthermore its performance are very near to the one of an oracle that optimizes the capacity over/under dimensioning parameter.

Multiview Fusion 3D Target Information Perception Model in Nighttime Unmanned Intelligent Vehicles

Unmanned technology is an important development project of today’s cutting-edge science and technology, which has a significant impact on social and economic development, national defense construction, and scientific and technological development. The rapid development of industrial information technology has driven the unmanned intelligent vehicle system to innovate and gradually enter the public’s view, and at the same time, the driving safety of unmanned intelligent vehicles is also widely concerned. Target information perception system is the foundation of unmanned system and the fundamental guarantee of safety and intelligence of unmanned vehicles. There are three key problems of target recognition in unmanned driving, namely, target classification, localization, and attitude determination. In the implementation of a networked virtual environment system, a flexible and complete perception model is needed as the guiding model of the system. Since 3D point cloud data can provide more spatial information than 2D RGB image data, it is more beneficial to determine the target category, position, and pose in 3D. In this paper, based on the existing research of unmanned intelligent vehicle perception system, we combine the application of fusion of 3D target information perception model and develop a nighttime unmanned system based on multiview fusion of 3D target information perception model. This system can simultaneously perform the detection of multiple categories of objects and predict the center point, length, width, height, and orientation of the objects, so that the unmanned car can sense the location of the surrounding objects when driving in the actual scene.

Resource Fragmentation-Aware Embedding in Dynamic Network Virtualization Environments

In network virtualization environments, with random arrival and departure of virtual network requests, there exist some resources (i.e., link resources and node resources) isolated from others in substrate networks. This phenomenon is referred to as resource fragmentation. In this paper, we attempt to improve the resource utilization efficiency by avoiding resource fragmentation in substrate networks. First and most importantly, we define a new metric called resource fragmentation degree (RFD) to quantitatively measure the status of resource fragmentation at substrate nodes and links. The basic idea of RFD is that the resource availability of a node (or a link) is determined by the residual link and node resources around the node (or the link). Based on the definition of RFD, we formulate the virtual network embedding (VNE) problem as a mixed integer programming problem with consideration of the cost of resource fragmentation. Then, an online VNE algorithm with consideration of RFD (VNERFD) is proposed to solve the problem, which is performed according to the current resource status of substrate networks and virtual network requests. To reduce accumulated fragmented resources produced by dynamic arrival and departure of virtual network requests, a heuristic virtual network reconfiguration algorithm based on RFD (VNR-RFD) is proposed. Simulation results show that VNE-RFD and VNR-RFD can effectively reduce fragmented resources and thus embed more virtual networks into substrate networks.

Automated Computer Network Exploitation with Bayesian Decision Networks

Penetration Testing (pentesting) is the process of using tactics and techniques to penetrate computer systems and networks to expose any issues in their cybersecurity \cite{rsa}. It is currently a manual process requiring significant experience and time that are in limited supply. One way to supplement the shortage is through automation. This paper presents the Automated Network Discovery and Exploitation System (ANDES) which demonstrates that it is feasible to automate the pentesting process. The uniqueness of ANDES is the use of Bayesian decision networks to represent the pentesting domain and subject matter expert knowledge. ANDES conducts multiple execution cycles, which build upon previous action results. This process simulates the iterative thinking process of human attackers. Cycles begin by modeling the current belief state using Bayesian decision networks. ANDES uses these networks to select and execute an expected best action. Observed results are used to update the systems current belief state before the next cycle begins. ANDES was tested in a live-execution event, taking place within a virtual network environment designed to mimic a small business’s internal network. ANDES successfully performed a series of information gathering and remote exploit actions, across multiple network hosts, to gain access to the objective target.

Advanced Co-Simulation Platform for UAV Simulations Under Virtual Wireless Network Environments

In this paper, we propose an advanced co-simulation platform that performs the unmanned aerial vehicle (UAV) simulation and the wireless network simulation concurrently. As UAV-based applications become more advanced and complicated and the commands and data transmitted between UAV and ground control station significantly increase, it is vital to safely and robustly transmit the packets carrying commands and data in a wireless network environment. However, the conventional UAV simulation platforms do not take into account the wireless network conditions encountered in the real-world flight so they cannot sufficiently simulate packet transmission. To address this issue, co-simulation platforms have been developed to verify UAV-based applications under a virtual wireless network environment. However, they still cannot effectively support the co-simulation needed to develop the UAV-based applications. In this paper, we have derived a list of improvements to the conventional co-simulation platform based on analysis of the conventional ones. Based on these improvements, we have developed an advanced co-simulation platform consisting of a UAV simulation part, a wireless network simulation part, and an integration part. In this platform, a two-channel approach was introduced to parallelize the synchronization required for performing the UAV simulation and the wireless network simulation simultaneously. The experimental results carried out on three test scenarios demonstrate that the proposed platform accurately performs co-simulation by efficient interoperation of its three parts and by remarkable reduction of the processing time delay, and that it is extendible due to its modularized software architecture.

Image Search System Based on Machine Learning

Since the 21st century, with the continuous maturity of network technology and its integration with the education field, traditional face-to-face communication has gradually expanded to the virtual network environment. In the online learning environment, students can use the online platform to communicate directly with teachers, no longer limited by time and region. The time and space breakthrough of teacher-student interaction has brought development opportunities for teachers to constantly contact students with a long-term management mechanism. Based on this situation, this article uses artificial intelligence technology to build a network communication platform. This article first analyzes the application status of artificial intelligence technology in the network communication platform, and then introduces the artificial intelligence technology applied in this article. Then, this article uses artificial intelligence technology to design a network communication platform, and test the function and performance of the platform. The test results show that the function of the system is very accurate and reliable, and the performance of the system is sufficient to support nearly 10,000 users at the same time.