Virtual Private Network Research Articles

Penerapan VPN Dalam Topologi Star Untuk Keamanan Pengiriman Data

Building a computer network needs significant care in selecting a configuration to ensure its efficacy and efficiency. It involves decisions about network topology, types, and device use. Security measures are essential for protecting networks with internet access from external attacks. A study conducted at an Islamic university in Indonesia indicated the use of a star network architecture and a client-server network model. The servers utilize Debian Linux. The university's security infrastructure includes a server authentication system and the setting up of a Virtual Private Network (VPN). If the server authentication difficulty is caused by a power outage, it is recommended that you use an UPS (uninterruptible power supply) to preserve power stability. Overall, security methods show up to be more effective when firewalls are installed.

Detection of Virtual Private Networks Encrypted Traffic using Machine and Ensemble Learning Algorithms

Abstract: The widespread adoption of Virtual Private Networks (VPNs) for secure communication over public networks has highlighted the need for accurate detection of encrypted network traffic. This study proposes a comprehensive approach utilizing machine and ensemble learning algorithms to address this detection challenge. Leveraging the ISCX VPN dataset, the project aims to develop a robust system capable of distinguishing between VPN and non-VPN traffic with high accuracy. Key stages include data acquisition, preprocessing, feature extraction, and model training using Decision Tree, Naive Bayes, Random Forest, and Adaboost algorithms. Through rigorous experimentation, the most effective algorithmic approach is identified. The project also offers insights into the diverse nature of encrypted network communication and its implications for network security. The anticipated outcomes include improved understanding and management of VPN traffic, enhancing overall network security and performance. This research contributes to advancing network security practices by offering practical solutions for encrypted traffic detection in real-world settings.

The use of Virtual Private Network in the Context of “Bring Your Own Device” in the Post Covid-19 Remote Workplace

Using a Virtual Private Network (VPN) for remote work provides an added layer of security by encrypting the internet connection. It helps protect sensitive data and prevents unauthorized access. With a VPN, employees can securely access company resources and files from anywhere in the world. This allows for greater flexibility in work arrangements and enables employees to collaborate seamlessly. Implementing a VPN as a remote workplace solution can lead to cost savings for businesses. It eliminates the need for physical office space and reduces expenses associated with commuting and travel. While VPNs offer numerous benefits for remote work, it is crucial for organizations to implement proper security measures and educate employees on best practices to realize this technology's full potential. This article explores the change in the use of VPNs during and after the Covid-19 pandemic era. A short literature review precedes the opinion of the information security community about the usage and role of VPNs in efficiently securing information and information systems.

VIRTUAL PRIVATE NETWORKS (VPN): A CONCEPTUAL REVIEW OF SECURITY PROTOCOLS AND THEIR APPLICATION IN MODERN NETWORKS

Virtual Private Networks (VPNs) play a crucial role in ensuring secure communication over public networks. This concept paper provides a comprehensive review of security protocols used in VPNs and their application in modern networks. The paper begins by defining VPNs and explaining their importance in securing data transmission over untrusted networks. It then discusses the evolution of VPN technology, highlighting the transition from traditional IPSec and SSL/TLS protocols to more modern approaches such as WireGuard and QUIC. The paper also examines the security protocols used in VPNs, including IPSec, SSL/TLS, and the newer protocols like WireGuard and QUIC. Each protocol is analyzed in terms of its security features, performance, and suitability for different use cases. Furthermore, the paper explores the application of VPNs in modern networks, including their use in remote access, site-to-site connectivity, and securing cloud-based services. It also discusses the challenges and considerations involved in deploying and managing VPNs in today's complex network environments. In conclusion, the paper emphasizes the importance of VPNs in ensuring secure communication in modern networks. It highlights the need for organizations to carefully evaluate their VPN requirements and choose the appropriate security protocols and technologies to protect their data and networks. Keywords: VPN, Security, Applications, Network.

Using Deep Learning Technology to Optimize VPN Networks Based on Security Performance

In recent years, network developments and user growth have increased network security problems and techniques. The trend in network security is towards web-based networks, given Internet users' diverse origins, unpredictable persons are more likely to participate in malevolent activities. Security and privacy safeguards are implemented using many technologies. This paper proposes using a virtual private network (VPN) to secure particular communications across vast networks. VPNs restrict unauthorised connections, benefiting secured hosts. Through a VPN network, connections can be kept hidden and external connections prohibited. The influence of a virtual private network (VPN) on a standard network's performance is studied by producing and assessing CBR, HTTP, and FTP payloads. The evaluation used throughput and time delay as performance measures after analysis of the finding, deep learning (DL) can predict attacks. Because that learns attack patterns during training to effectively forecast attacks. To detect attacks, deep learning-based attack prevention model was created This method uses Nave Bayes and FFNN to enhace network performance. The results show that VPNs affect packet latency and performance differently depending on the data type. The FFNN algorithm detects intrusions with 98% accuracy.

Full‐mesh VPN performance evaluation for a secure edge‐cloud continuum

AbstractThe recent introduction of full‐mesh virtual private network (VPN) solutions which offer near native performance, coupled with modern encryption algorithms and easy scalability as a result of a central control plane have a strong potential to enable the implementation of a seamless edge‐cloud continuum. To test the performance of existing solutions in this domain, we present a framework consisted of both essential and optional features that full‐mesh VPN solutions need to support before they can be used for interconnecting geographically dispersed compute nodes. We then apply this framework on existing offerings and select three VPN solutions for further tests: Headscale, Netbird, and ZeroTier. We evaluate their features in the context of establishing an underlay network on top of which a Kubernetes overlay network can be created. We test pod‐to‐pod TCP and UDP throughput as well as Kubernetes application programming interface (API) response times, in multiple scenarios, accounting for adverse network conditions such as packet loss or packet delay. Based on the obtained measurement results and through analysis of the underlying strengths and weaknesses of the individual implementations, we draw conclusions on the preferred VPN solution depending on the use‐case at hand, striking a balance between usability and performance.

Using a nested virtualization tool for teaching VPN fundamentals

A common problem of undergraduate courses about security and computer networks is the difficulty of providing practical exercises to students. Although different approaches have been followed to solve this problem, it is still an open issue. In this work, we first discuss several possible open-source simulation/emulation/virtualization tools that could be used in the design of a lab session for teaching Virtual Private Networks (VPN) fundamentals based on OpenVPN. Next, we describe how a hands-on lab about VPNs can be carried out with the selected virtualization tool, called NETinVM. The proposed lab starts installing the OpenVPN packages in client and server machines, as in a real-case scenario. Although in this case both client and servers are Kernel-based Virtual Machines (KVM) connected to networks inside NETinVM. The nested virtualization included in NETinVM allows to deploy all the required types of VPNs within the virtual machine, allowing the students to reproduce the lab without the need of a network infrastructure, and following exactly the same process required in a real scenario. The evaluation results show that the level of learning achieved by the students through this lab session seems to be high, and they consider this lab useful for their professional development, showing that virtualization technology can help in computer network education.

Data resource profile of an online database system for forensic mental health services

This paper introduces a forensic psychiatry database established in Japan and discusses its significance and future issues. The purpose of this Database, created under the Medical Treatment and Supervision Act (MTSA) Database Project, is to improve the quality of forensic psychiatry treatment. It can collect monthly data on “basic information,” “Orders and hospitalizations under the MTSA,” “Treatment process,” “Criminal and medical treatment history,” and “problematic behavior in the unit.” The online system has accumulated data on more than 8,000 items in 24 broad categories. Medical data are exported from the medical care assisting system of 32 designated inpatient facilities in XML format and then saved on USB memory sticks. The files are imported into the Database system client, which sends the data to the Database server via a virtual private network. This system minimizes errors and efficiently imports patient data. However, there is a limitation that it is difficult to set items that need to be analyzed to solve everyday clinical problems into the database system because they tend to change over time. By evaluating the effectiveness of the Database, and collecting appropriate data, it is expected to disseminate a wide range of knowledge that will contribute to the future development of mental health and welfare care.

RT-CBCH: Real-Time VPN Traffic Service Identification Based on Sampled Data in High-Speed Networks

Virtual Private Network (VPN) technology can bypass censorship and access geographically locked services. Some harmful information may be hidden in VPN traffic and circumvent the surveillance systems, bringing a significant challenge to network security. Considering the increasing richness of service types in VPN traffic, identifying traffic service facilitates further targeting harmful VPN traffic. Therefore, VPN traffic service identification is critical in network management. The existing identification methods use complete traffic for analysis. However, massive data analysis in high-speed networks consumes enormous resources, limiting the real-time processing of traffic identification. This paper proposes a real-time VPN traffic service identification method named RT-CBCH. We construct features that are still available after sampling and design a fast traffic processing structure based on Counting Bloom Filter and Chained Hash Table (CBCH). Experimental results validate the real-time capability, stability and accuracy of our method. At the sampling ratio of 1/256, it takes only 23.63 seconds to process the mixed traffic of 900-second traffic generated on a 10 Gbps link and our collected V2Ray traffic, which is increasingly common in VPN traffic. Under different sampling ratios, the identification results remain respectable, with an overall accuracy of about 90% for application service and over 99% for V2Ray proxy service. Furthermore, comparisons with similar work illustrate the high accuracy and low resource consumption of RT-CBCH. Experimental results show that our method can stably implement real-time VPN traffic service identification from sampled data in high-speed networks.

Security at the Edge for Resource-Limited IoT Devices.

The Internet of Things (IoT) is rapidly growing, with an estimated 14.4 billion active endpoints in 2022 and a forecast of approximately 30 billion connected devices by 2027. This proliferation of IoT devices has come with significant security challenges, including intrinsic security vulnerabilities, limited computing power, and the absence of timely security updates. Attacks leveraging such shortcomings could lead to severe consequences, including data breaches and potential disruptions to critical infrastructures. In response to these challenges, this research paper presents the IoT Proxy, a modular component designed to create a more resilient and secure IoT environment, especially in resource-limited scenarios. The core idea behind the IoT Proxy is to externalize security-related aspects of IoT devices by channeling their traffic through a secure network gateway equipped with different Virtual Network Security Functions (VNSFs). Our solution includes a Virtual Private Network (VPN) terminator and an Intrusion Prevention System (IPS) that uses a machine learning-based technique called oblivious authentication to identify connected devices. The IoT Proxy's modular, scalable, and externalized security approach creates a more resilient and secure IoT environment, especially for resource-limited IoT devices. The promising experimental results from laboratory testing demonstrate the suitability of IoT Proxy to secure real-world IoT ecosystems.

Optimizing Secure Communication in Distributed Corporate Networks through PPTP and IPSec VPN Protocols

In today’s industry, company’s activities are scattered in several locations and need internet for communication purposes. However, it will be dangerous if third-parties have access to their information. One of the solutions for securing information exchange is by using Virtual Private Network (VPN). The VPN protocols that will be used are Point-to-Point Tunneling Protocol (PPTP) with the help of Internet Protocol Security (IPSec). These protocols are simulated in VirtualBox that has been previously installed with two Mikrotik routers and two Windows 7 operating systems. The PPTP and IPSec protocols are configured in both routers through Winbox software. For the computers, the IP addresses are configured along with iPerf software for bandwidth analysis. In this VPN simulation, both computers can communicate with each other and have no problem in accessing the internet. In PPTP’s network, the average bandwidth is 1.293 Mbits/sec. Meanwhile, in PPTP with IPSec’s network, the average bandwidth is 0.853 Mbits/sec. There is a slight difference considering that in PPTP, the data packets are only encapsulated. On the other hand, the data packets in IPSec protocol are both encapsulated and encrypted.

Virtual Private Network—Firewall Integration for Wireless Local Area Network Improvement against Jammers

Wireless networks are susceptible to many security problems since the wireless medium is open. Jammer (jamming attack) is the most important problems in wireless networks. It is denial-of-service attack in which attackers send malicious signals or messages on a channel that is intended for normal traffic by intentionally interfering with the network. One of the solutions is the development of tunneling technology as an attempt to secure communication networks. Virtual Private Network (VPN) tunneling technology when integrated with firewall would provide a suitable security. In this study, number of Jammers would interfere with the normal operation of the network resulting in performance degradation of network. These Jammers decrease throughput and increase delay and data dropped. The aim of this study is to improve the performance of Wireless Local Area Network (WLAN) by integrating Firewall with VPN in number of different Riverbed Modeler simulation scenarios which it is utilized in this study for video and data applications. Firewall is a technology employed to regulate the level of network connectivity. It can block unauthorized access from an external network to an internal network’s resources. In this study, the tunneling technology of VPN was integrated with firewall to improve the performance by increasing the throughput and decreasing delay and data dropped. The firewall would block any access to the server from the workstations and the VPN would filter the packets to allow a specific access to the specific server. The results showed that VPN when integrated with firewall increased the throughput from 1,350,000 to 1,500,000 bits/sec and decreased the delay from 0.0054 sec to 0.0047 sec and data dropped from 7,800 to 5,000 bits/sec so that this study proved that VPN incorporated with firewall provides a good throughput and delay improvement for wireless local area network.

Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification

With the increasing prevalence of encrypted network traffic, cyber security analysts have been turning to machine learning (ML) techniques to elucidate the traffic on their networks. However, ML models can become stale as new traffic emerges that is outside of the distribution of the training set. In order to reliably adapt in this dynamic environment, ML models must additionally provide contextualized uncertainty quantification to their predictions, which has received little attention in the cyber security domain. Uncertainty quantification is necessary both to signal when the model is uncertain about which class to choose in its label assignment and when the traffic is not likely to belong to any pre-trained classes. We present a new, public dataset of network traffic that includes labeled, Virtual Private Network (VPN)-encrypted network traffic generated by 10 applications and corresponding to 5 application categories. We also present an ML framework that is designed to rapidly train with modest data requirements and provide both calibrated, predictive probabilities as well as an interpretable "out-of-distribution" (OOD) score to flag novel traffic samples. We describe calibrating OOD scores using p-values of the relative Mahalanobis distance. We demonstrate that our framework achieves an F1 score of 0.98 on our dataset and that it can extend to an enterprise network by testing the model: (1) on data from similar applications, (2) on dissimilar application traffic from an existing category, and (3) on application traffic from a new category. The model correctly flags uncertain traffic and, upon retraining, accurately incorporates the new data.

Designing a System to Facilitate the process of Connecting 4.0-Generation Industrial Machines to the internet

Industry 4.0 aims to create more efficient and flexible manufacturing processes that can respond quickly to changes in demand and customer requirements. The integration of digital technologies and physical systems allows data to be collected, analysed, monitored and controlled in real time. This can increase productivity, reduce downtime and improve product quality. In this paper, remote control, monitoring and online data storage of industrial machines based on human-machine interface (HMI) module are facilitated. HMI integrates the control system with the Industrial Internet of Things (IIOT). On the other hand, it can be controlled and monitored anywhere in the world via the Internet using a Virtual Private Network (VPN) to open different navigation private channel and HMI interface. It was clear from the collection of recorded data and the results of practical testing that the designed system facilitated the process of accessing the industrial machine

The method of assessing the efficiency of tunneling management in trunk networks

The article deals with the method of evaluating the effectiveness of tunneling management in trunk networks with a focus on improving efficiency and optimizing data transmission. The relevance of creating a tunneling management technique in the context of growing traffic volumes and expanding the functionality of IP/MPLS networks is substantiated. It is noted that traditional control methods may be ineffective in the given environment, and the use of specialized models of network construction is a key factor for optimal use of tunnel resources. The main requirements for the trunk network technology are high bandwidth, speed of information transfer and scalability. The current state of the market and the needs of users require access to integrated network services, and the organization of virtual private networks, the provision of intelligent services. The growing demand for additional services implemented on top of simple IP access requires the development of backbone network architectures with virtually unlimited scaling capabilities, increased traffic processing speed, and unprecedented flexibility in terms of the organization of additional services. The MPLS technology allows for network integration, due to which service providers will not only be able to save the means invested in asynchronous transmission equipment, but also get additional benefits from the compatible use of protocols. It is proposed to use a specialized model to determine and optimize tunnel resources aimed at maintaining a high level of service quality. It is shown that with an increase in the load with an unchanged number of nodes, the time the packet stays in the tunnel increases, since the load served by each node increases, and therefore the total time the packet stays in the tunnel.

Webometric analysis of alternative access to scholarly publication

Purpose: This study aims to uncover the worldwide usage patterns and growth trends of Library Genesis (LibGen) and Sci-Hub, two popular alternative access platforms to scholarly publications. Design/methodology/approach: This study utilizes a webometric approach to analyze worldwide usage patterns and growth trends of LibGen and Sci-Hub. Data was collected between May and June 2023 on online presence, usage metrics, and pattern from different web-based tools like LibGen and Sci-Hub database, Google Trends, SimilarWeb, and existing Python and GitHub projects. Analyses are conducted by incorporating statistical techniques and previous literature reviews. Findings: The study reveals that LibGen and Sci-Hub have experienced a notable surge in popularity despite facing copyright infringements, legal disputes, and restrictions. Conversion rates and unique visitors have significantly increased, with users from various nations, including developed countries like the USA and China. Statistical data shows a preference for accessing science and technology-related resources, particularly in the field of medicine. Most of the downloads originated from reputable publishers and academic journals. The main motivations behind using these platforms are the high costs and limited access to scholarly publications. This phenomenon has attracted researchers, academics, students, and information seekers globally as they seek to overcome financial constraints and institutional barriers hindering access to valuable knowledge. Research limitations: The study acknowledges limitations related to potential discrepancies and biases from external software and incomplete representation of source databases. The dynamic nature of the platforms also means data is only up until 2022, potentially excluding recent developments. Additionally, the country specific data may not give the exact idea as people use Virtual Private Network (VPN) to access the platforms bypassing restrictions. Practical implications: The findings shed light on the challenges faced by the academic community and offer potential implications for policymakers, publishers, and researchers aiming to address the growing demand for affordable and accessible scholarly publications. Originality/value: The originality of this study lies in its application to examine the alternative shadow libraries for scholarly publications. This research contributes to the existing knowledge domain by offering valuable insights into the extent and trends of utilizing shadow platforms, focusing on the implications of copyright infringements, legal concerns, and restrictions on scholarly knowledge dissemination.

Analisis Kepuasan Pengguna WhatsApp Web Menggunakan Metode Webqual 4.0 dan IPA

Social media has become a lifestyle that cannot be ignored in recent times. One of the most frequently used online communication tools today is WhatsApp, which was created in 2009 by Jan Koum. In Indonesia, this application is used by all groups ranging from young and old, students to workers / employees who indirectly WhatsApp is a social media that has many advantages to become quite important in everyday life in society. The emergence of WhatsApp Web is a technology that gives its users the freedom to use more than one device, so that it can attract more users to use it. With so many users, researchers want to know how user satisfaction from this website regarding its usability, appearance, and service quality. Of course, to analyze the research, several methods are needed so that the analysis can run well. This research integrates the WebQual 4.0 and Importance Performance Analysis methods as a data analysis process. Researchers also used a questionnaire as a way to collect data from WhatsApp Web users, the questionnaire consisted of 23 questions with two indicators of expectations and performance. The results of this study are that there is a good influence from Usability Quality and Services Interaction Quality on user satisfaction and another case with Information Quality which has no effect. The recommendation for users is that WhatsApp Web cannot be accessed via a browser on a PC or laptop, it is possible that there is a VPN (Virtual Private Network) application, this application must be deactivated first.

A Biobanking System for Diagnostic Images: Architecture Development, COVID-19-Related Use Cases, and Performance Evaluation.

Systems capable of automating and enhancing the management of research and clinical data represent a significant contribution of information and communication technologies to health care. A recent advancement is the development of imaging biobanks, which are now enabling the collection and storage of diagnostic images, clinical reports, and demographic data to allow researchers identify associations between lifestyle and genetic factors and imaging-derived phenotypes. The aim of this study was to design and evaluate the system performance of a network for an operating biobank of diagnostic images, the Bio Check Up Srl (BCU) Imaging Biobank, based on the Extensible Neuroimaging Archive Toolkit open-source platform. Three usage cases were designed focusing on evaluation of the memory and computing consumption during imaging collections upload and during interactions between two kinds of users (researchers and radiologists) who inspect chest computed tomography scans of a COVID-19 cohort. The experiments considered three network setups: (1) a local area network, (2) virtual private network, and (3) wide area network. The experimental setup recorded the activity of a human user interacting with the biobank system, which was continuously replayed multiple times. Several metrics were extracted from network traffic traces and server logs captured during the activity replay. Regarding the diagnostic data transfer, two types of containers were considered: the Web and the Database containers. The Web appeared to be the more memory-hungry container with a higher computational load (average 2.7 GB of RAM) compared to that of the database. With respect to user access, both users demonstrated the same network performance level, although higher resource consumption was registered for two different actions: DOWNLOAD & LOGOUT (100%) for the researcher and OPEN VIEWER (20%-50%) for the radiologist. This analysis shows that the current setup of BCU Imaging Biobank is well provisioned for satisfying the planned number of concurrent users. More importantly, this study further highlights and quantifies the resource demands of specific user actions, providing a guideline for planning, setting up, and using an image biobanking system.

Safeguarding Transactional Data: A Comprehensive Investigation of VPN Integration in Point-of-Sale Systems for Enhanced Security and Connectivity

The integration of Virtual Private Network (VPN) technology into Point-of-Sale (POS) systems has emerged as a solution for securing transactional data in businesses. This study investigates the implementation of a VPN server to address security and connectivity challenges faced by the University of St. La Salle (USLS) Bookstore. The research delves into VPN policy frameworks, assessing the scope and binding, authentication methods, and VPN credentials and security protocols. The system design employs a combination of conceptual models and activity diagrams to illustrate the process of user authentication and data encryption. Furthermore, the study evaluates the system's performance in terms of scalability, efficiency, and effectiveness in ensuring authentication and security. The deployment of the VPN infrastructure involves the installation of requisite software and hardware components, along with rigorous testing procedures to assess system performance and reliability. The findings highlight the significance of VPN in safeguarding sensitive transaction data and enhancing the efficiency of POS operations. The study's outcomes offer valuable insights into the effective implementation and management of VPN technology for securing business operations.

Security Methods to Improve Quality of Service

INTRODUCTION: Security and Quality of Service (QoS) are two of the most critical aspects of communication networks. Security measures are implemented to protect the network from unauthorized access and malicious attacks, whereas QoS measures are implemented to ensure that the network is reliable, efficient, and can meet the demands of users. 
 OBJECTIVES: This paper examines various methods of network security and their impact on the quality of service (QoS) in computer networks. The study analyses different types of network attacks, such as denial of service (DoS), distributed denial of service (DDoS), and intrusion attempts, and their impact on QoS. The paper also explores various security mechanisms, such as intrusion detection and prevention systems (IDPS), firewalls, virtual private networks (VPNs), and techniques for encryption, that can help mitigate network security threats while maintaining QoS.METHODS: The study evaluates the strengths and weaknesses of the security mechanisms in terms of their ability to provide protection against network attacks while minimizing the impact on QoS. 
 RESULTS: The paper provides recommendations for organizations to enhance their network security posture while improving the QoS, such as implementing robust network security policies, investing in advanced security tools, and training employees to recognize and respond to network security incidents.
 CONCLUSION: This paper offers a comprehensive analysis of network security methods and their impact on QoS, providing insights and recommendations for organizations to improve their network security posture and maintain a high level of QoS.hese are the conclusions of this paper.