Network Administrators Research Articles

Gotta Catch ’em All: A Multistage Framework for Honeypot Fingerprinting

Honeypots are decoy systems that lure attackers by presenting them with a seemingly vulnerable system. They provide an early detection mechanism as well as a method for learning how adversaries work and think. However, over the past years, several researchers have shown methods for fingerprinting honeypots. This significantly decreases the value of a honeypot; if an attacker is able to recognize the existence of such a system, they can evade it. In this article, we revisit the honeypot identification field, by providing a holistic framework that includes state-of-the-art and novel fingerprinting components. We decrease the probability of false positives by proposing a rigid multi-step approach for labeling a system as a honeypot. We perform extensive scans covering 2.9 billion addresses of the IPv4 space and identify a total of 21,855 honeypot instances. Moreover, we present several interesting side findings such as the identification of around 355,000 non-honeypot systems that represent potentially misconfigured or unpatched vulnerable servers (e.g., SSH servers with default password configurations and vulnerable versions). We ethically disclose our findings to network administrators about the default configuration and the honeypot developers about the gaps in implementation that lead to possible honeypot fingerprinting. Last, we discuss countermeasures against honeypot fingerprinting techniques.

Detection of obfuscated Tor traffic based on bidirectional generative adversarial networks and vision transform

The Onion Router (TOR) network is a decentralized system of volunteer-run servers that aims to protect the anonymity and privacy of users by routing their internet traffic through a series of nodes. Individuals who use the TOR network may employ obfuscated traffic to conceal their internet activity from network administrators or security systems attempting to block or monitor them. Furthermore, some may use obfuscated Tor traffic to hide illegal activities, such as buying and selling illegal goods or accessing illegal services on the dark web. Despite efforts to identify and block Tor traffic, challenges remain, such as a limited set of features for identification, leading to false positives and negatives. To address these challenges, this paper proposes a novel approach using Visual Transformation (ViT), and augmentation by Bidirectional Generative Adversarial Networks (BiGAN). The proposed approach demonstrates superior performance on the ISCX-Tor2016 dataset, achieving 99.59% accuracy, 99.83% recall, 99.72% precision, and 99.78% F-score, thereby outperforming current state-of-the-art techniques.

Implementation Of The Dude Application As A Network Management System

The development of information technology and especially networks is very rapid. Therefore, there is a demand for a sophisticated computer network system. Where the admin cannot know the condition of each user's computer and whether the existing service system is on or off. Display and sound are technologies for detecting network damage on company computers. Implementation of a computer network client monitoring system using display and sound allows the system to send reports when a client computer or service shuts down automatically to the admin without involving the user. For this reason, researchers are trying to develop The Dudemonitoring network application in the form of display and sound. The results obtained from using this network monitoring application are monitoring the services carried out by each host or IP address. If a network error is correctly detected, a notification will be sent to the network administrator, then the network administrator will be able to repair network damage..

Enhancing LAN Failure Predictions with Decision Trees and SVMs: Methodology and Implementation

Predicting Local Area Network (LAN) equipment failure is of utmost importance to ensure the uninterrupted operation of modern communication networks. This study explores the use of machine learning algorithms to enhance the accuracy of equipment failure prediction in LAN environments. Using these algorithms to enhance LAN failure predictions involves collecting and analyzing network data, such as packet loss rates and latency, to identify patterns and anomalies. These algorithms can then predict potential LAN failures by recognizing early warning signs and deviations from normal network behavior. By leveraging machine learning, network administrators can proactively address issues, reduce downtime, and improve overall network reliability. In our study, two powerful machine learning algorithms—decision tree and support vector machine (SVM)—are used. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising various LAN equipment parameters and corresponding failure instances is utilized. The dataset is pre-processed to handle missing values and normalize features, ensuring the algorithms’ optimal performance. Performance metrics, such as accuracy, precision, recall, and F1-score, are employed to assess the predictive capabilities of the models. The excremental results of our study lead to more reliable and stable network operations by allowing early detection of potential issues and preventive maintenance. This leads to reduced downtime, improved network performance, and enhanced overall user satisfaction. They demonstrate the efficacy of both decision tree and SVM algorithms in accurately predicting LAN equipment failure.

Exploring Factors Influencing Gen Z's Continuation Intention on TikTok Social Network: A Case Study in Can Tho City, Vietnam

The aim of this study is to gain a more profound understanding of the factors that motivate individuals from Generation Z, commonly known as Gen Z, to persistently engage with the popular social media platform TikTok. To ensure a comprehensive analysis, a quantitative research approach was employed, involving a sample size of 316 participants from Generation Z. The research employed confirmatory factor analysis, structural equation modelling, and Cronbach's alpha to validate the anticipated factor structure, examine the relationships between variables, and determine the reliability of the measurement scales. Furthermore, the study employed a combination of exploratory factor analysis, confirmatory factor analysis, and structural equation modelling to uncover latent characteristics. The data analysis utilised the software programmes Amos and SPSS. During the testing phase, various traits such as digital interaction, archiving, self-actualization, escapism, and gratification were considered. The study's outcomes provide novel insights into previously undisclosed information and shed light on the motivations behind Generation Z's persistent use of the TikTok social media platform. These findings may prove valuable to social media network administrators and developers in their endeavours to enhance user engagement on their platforms. Keywords: Continuation intention, TikTok, Social network, Social network, Social media platform.

A Hypertuned Lightweight and Scalable LSTM Model for Hybrid Network Intrusion Detection

Given the increasing frequency of network attacks, there is an urgent need for more effective network security measures. While traditional approaches such as firewalls and data encryption have been implemented, there is still room for improvement in their effectiveness. To effectively address this concern, it is essential to integrate Artificial Intelligence (AI)-based solutions into historical methods. However, AI-driven approaches often encounter challenges, including lower detection rates and the complexity of feature engineering requirements. Finding solutions to overcome these hurdles is critical for enhancing the effectiveness of intrusion detection systems. This research paper introduces a deep learning-based approach for network intrusion detection to overcome these challenges. The proposed approach utilizes various classification algorithms, including the AutoEncoder (AE), Long-short-term-memory (LSTM), Multi-Layer Perceptron (MLP), Linear Support Vector Machine (L-SVM), Quantum Support Vector Machine (Q-SVM), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). To validate the effectiveness of the proposed approach, three datasets, namely IOT23, CICIDS2017, and NSL KDD, are used for experimentation. The results demonstrate impressive accuracy, particularly with the LSTM algorithm, achieving a 97.7% accuracy rate on the NSL KDD dataset, 99% accuracy rate on the CICIDS2017 dataset, and 98.7% accuracy on the IOT23 dataset. These findings highlight the potential of deep learning algorithms in enhancing network intrusion detection. By providing network administrators with robust security measures for accurate and timely intrusion detection, the proposed approach contributes to network safety and helps mitigate the impact of network attacks.

Towards zero downtime: Using machine learning to predict network failure in 5G and beyond

A stable network is very important to both network providers and their customers, as it increases reliability, improves security and helps customers and companies save costs. When network outages occur, they result in significant downtime and financial losses for organizations and network users. Traditional methods of detecting and troubleshooting network failures are often reactive and time-consuming, whereby network administrators rely on traditional methods such as reactive monitoring and manual troubleshooting. These methods are often not effective in detecting and preventing network failures. In this paper, we propose a machine learning-based approach to predict network failures and minimize downtime. Network performance observability data from a 5G core network testbed based on Cloud-native Network Functions (CNFs) is used to train several supervised learning models, including random forest, gradient boosting regressor, conventional support vector regressor and proposed support vector regressor, to predict network failures. Our experiments and analysis show that the proposed model Support Vector Regressor (SVR) produced better results as compared to other models. In a very short amount of time (ten seconds), the proposed SVR model is capable of predicting whether a network failure event will occur or not within the next ten minutes, with an f1-score of more than 0.9. Our results indicate that machine learning-based approaches can significantly enhance the detection and prediction of network failures, leading to zero downtime and improved network performance.

Interactive didactic support for students in a digital learning environment

Introduction. In recent years, digitalization has been actively developing in the system of professional education and takes an important role in the UNESCO strategy related to culture, science and education. The learning process in the digital educational environment is being transformed, a new level of quality is being acquired. The sustainable development of educational systems of students actualizes the improvement of the quality of education in the digital educational environment, which are actively developed within the framework of digital didactics The purpose of the study is to identify the features of the implementation of interactive didactic support for students for improvement of distance learning format. Materials and methods. In accordance with the statements of the activity, system and information approaches, an author's questionnaire has been developed. The survey involved 100 first- and second-year students of the Moscow State Educational Complex studying in the field of study "Network and system administration". The age of the respondents is from 17 to 20 years, among which 30% are women. The survey was conducted using the Google Forms tool. The involvement of students in educational activities in the conditions of distance and traditional formats of education was studied at the first stage. At the second stage, the preference of students in the choice of forms of didactic support was revealed: face-to-face consultations according to the schedule, group chat with the teacher, video conferences with the ability to ask questions, simulators for practicing problem solving methods, short video lectures. Respondents were asked to rank three of the five proposed items in order of importance. Results. Distance learning format was chosen by 46% of respondents, and mixed – by 54%. Most of the students (60%) indicated that they use other sources of information in the learning process. Among the forms of didactic support, it was revealed that students prefer face-to-face consultations with a teacher (average position score for 1st year students is 2.05, 2nd year students – 2.15), group chat (1.2 and 1.75 points, respectively) and videoconferencing with the ability to ask questions to the teacher (1.65 and 1.2 points). Less significant for students are short video lectures (0.8 and 0.75 points) and simulators for practicing problem solving methods (0.2 and 0.25 points). The results of the survey confirm that blended learning, in comparison with distance learning, has significant advantages. Conclusion. The development of the digital educational environment involves the adjustment of the professional competencies of the teacher so that he is in the leading subject position in the organization of didactic support for students. An important area of digital didactics is the improvement of interactive educational technologies for the effective support of students.

An Analytical Framework for Data Collection and Analysis in IP Network

Abstract The primary focus of the study is to investigate the importance of data collection and analysis in IP networks for detecting, identifying, and responding to potential cyber attacks. It examines the use of the Suricata system’s integration with the process of sending detected anomalies to the non-relational Elasticsearch database. The research also looks into the use of Data Lakes, which are centralized storage systems capable of securely storing and analyzing massive amounts of IP traffic data in their native format. An experimental environment is presented, featuring the Elasticsearch database, REDIS cache, and Suricata IDS tool, to conduct experiments. The findings show that combining Suricata with Elasticsearch and Redis cache results a suitable combination, leading to enhanced performance and increased analysis accuracy. In conclusion, by leveraging the strengths of these technologies, it is possible to establish a robust and efficient infrastructure that effectively assists network administrators to safeguard networks against various cyber threats in the network environment.

ROAD INFRASTRUCTURE MANAGEMENT - THE PERSPECTIVE OF THE LOCAL ROADS AUTHORITY

This paper presents the results of research conducted among 100 local government units performing the duties of road network administrators, i.e., administrators of municipal roads (AMR), county roads (ACR) and those operating within the boundaries of cities with county rights (ACCR). The aim of the research was to identify and assess management problems related to the implementation of statutory measures related to the protection and development of respective road networks. A hypothesis was formulated about the existence of statistically significant differences between the AMR, ACR and ACCR groups, which was verified by quantitative analysis of the data obtained from the online survey. They were analysed and interpreted using the SPSS package and using measures of descriptive statistics, the correlation coefficient and the Kruskal–Wallis test by ranks. The results of the study indicate that the difficulties of providing and maintaining an efficient road network pavement, as well as the challenges of developing draft financing plans for the construction, reconstruction, and rehabilitation of the road network, are currently the main points of focus for all road administrators. The lack of statistical significance of intergroup differences was observed in the vast majority of measurements. The exceptions to this were the differences that occurred in the total number of difficulties reported, which were greater among the members of AMR and ACR than ACCR, as well as the differences related to the implementation of measures undertaken for the maintenance of roadside greenery, which were more onerous for ACR.

USER AWARENESS OF SECURITY AND PRIVACY IN SOCIAL NETWORKING SITES

The rapid expansion and widespread adoption of social networking sites have created new security and privacy challenges for individuals. This study will assess users' awareness of the security and privacy measures and their understanding of potential risks and vulnerabilities. The study uses a quantitative survey to collect data on users' perceptions, understandings, and experiences about security and privacy on social sites. The results will be analyzed to identify trends, knowledge gaps, and user perceptions, providing insight into the current landscape of user perceptions and insights. The outcome of this study will be useful in formulating plans and recommendations to improve security measures and promote responsible online behavior among social network administrators, policymakers, and users. This study aims to provide users with the necessary knowledge to adequately protect themselves in the digital age by highlighting their awareness and understanding of security and privacy on social networks.

Optimalisasi Website untuk Monitoring Jaringan OPD di Dinas Kominfo Kota Medan dengan Metode Triangulasi

This study aims to optimize the use of a website as a tool for monitoring the network of Local Government Agencies (OPD) at the Department of Communication and Informatics (Kominfo) of Medan City using the triangulation method. The triangulation method involves the use of observation, interviews, and documentation to gain a more comprehensive understanding of the effectiveness of the website as a network monitoring tool. During the observation phase, the researcher conducted direct observations of user interactions with the monitoring website and understood the dynamics of its usage. Interviews with users and network administrators were conducted to gain direct insights into the strengths and challenges in using the website. Technical data and network performance reports were also obtained from existing documentation. The results of this study illustrate how the triangulation method can provide a deeper understanding of the effectiveness of the monitoring website. Observation revealed user interaction patterns with the website interface, interviews provided user perspectives on usability and desired improvements, and documentation provided technical context. The conclusion of this study is that the use of the triangulation method yields a more comprehensive understanding of issues and opportunities in optimizing the website for OPD network monitoring. It is expected that the results of this study will provide valuable guidance for the development and enhancement of more effective website features for monitoring OPD networks. This contributes to better and more responsive information technology infrastructure management in the local government setting.

Exploring Path Computation Techniques in Software-Defined Networking: A Review and Performance Evaluation of Centralized, Distributed, and Hybrid Approaches

Software-Defined Networking (SDN) is a networking paradigm that allows network administrators to dynamically manage network traffic flows and optimize network performance. One of the key benefits of SDN is the ability to compute and direct traffic along efficient paths through the network. In recent years, researchers have proposed various SDN-based path computation techniques to improve network performance and reduce congestion.
 This review paper provides a comprehensive overview of SDN-based path computation techniques, including both centralized and distributed approaches. We discuss the advantages and limitations of each approach and provide a critical analysis of the existing literature. In particular, we focus on recent advances in SDN-based path computation techniques, including Dynamic Shortest Path (DSP), Distributed Flow-Aware Path Computation (DFAPC), and Hybrid Path Computation (HPC).
 We evaluate three SDN-based path computation algorithms: centralized, distributed, and hybrid, focusing on optimal path determination for network nodes. Test scenarios with random graph simulations are used to compare their performance. The centralized algorithm employs global network knowledge, the distributed algorithm relies on local information, and the hybrid approach combines both. Experimental results demonstrate the hybrid algorithm's superiority in minimizing path costs, striking a balance between optimization and efficiency. The centralized algorithm ranks second, while the distributed algorithm incurs higher costs due to limited local knowledge. This research offers insights into efficient path computation and informs future SDN advancements.
 We also discuss the challenges associated with implementing SDN-based path computation techniques, including scalability, security, and interoperability. Furthermore, we highlight the potential applications of SDN-based path computation techniques in various domains, including data center networks, wireless networks, and the Internet of Things (IoT).
 Finally, we conclude that SDN-based path computation techniques have the potential to significantly improvement in-order to improve network performance and reduce congestion. However, further research is needed to evaluate the effectiveness of these techniques under different network conditions and traffic patterns. With the rapid growth of SDN technology, we expect to see continued development and refinement of SDN-based path computation techniques in the future.

Improving Data Transmission Rate with Self Healing Activation Model for Intrusion Detection with Enhanced Quality of Service

Several types of attacks can easily compromise a Wireless Sensor Network (WSN). Although not all intrusions can be predicted, they may cause significant damage to the network and its nodes before being discovered. Due to its explosive growth and the infinite scope in terms of applications and processing brought about by 5G, WSN is becoming more and more deeply embedded in daily life. Security breaches, downed services, faulty hardware, and buggy software can all cripple these enormous systems. As a result, the platform becomes unmaintainable when there are a million or more interconnected devices. When it comes to network security, intrusion detection technology plays a crucial role, with its primary function being to constantly monitor the health of a network and, if any aberrant behavior is detected, to issue a timely warning to network administrators. The current network's availability and dependability are directly tied to the efficacy and timeliness of the Intrusion Detection System (IDS). An Intrusion-Tolerant system would incorporate self-healing mechanisms to restore compromised data. System attributes such as readiness for accurate service, supply identical and correct data, confidentiality, and availability are necessary for a system to merit trust. In this research, self-healing methods are considered that can detect intrusions and can remove with intellectual strategies that can make a system fully autonomous and fix any problems it encounters. In this study, a new architecture for an Intrusion Tolerant Self Healing Activation Model for Improved Data Transmission Rate (ITSHAM-IDTR) is proposed for accurate detection of intrusions and self repairing the network for better performance, which boosts the server's performance quality and enables it to mend itself without any intervention from the administrator. When compared to the existing paradigm, the proposed model performs in both self-healing and increased data transmission rates..

A Review of Blockchain Technology in Knowledge-Defined Networking, Its Application, Benefits, and Challenges

Knowledge-Defined Networking (KDN) necessarily consists of a knowledge plane for the generation of knowledge, typically using machine learning techniques, and the dissemination of knowledge, in order to make knowledge-driven intelligent network decisions. In one way, KDN can be recognized as knowledge-driven Software-Defined Networking (SDN), having additional management and knowledge planes. On the other hand, KDN encapsulates all knowledge-/intelligence-/ cognition-/machine learning-driven networks, emphasizing knowledge generation (KG) and dissemination for making intelligent network decisions, unlike SDN, which emphasizes logical decoupling of the control plane. Blockchain is a technology created for secure and trustworthy decentralized transaction storage and management using a sequence of immutable and linked transactions. The decision-making trustworthiness of a KDN system is reliant on the trustworthiness of the data, knowledge, and AI model sharing. To this point, a KDN may make use of the capabilities of the blockchain system for trustworthy data, knowledge, and machine learning model sharing, as blockchain transactions prevent repudiation and are immutable, pseudo-anonymous, optionally encrypted, reliable, access-controlled, and untampered, to protect the sensitivity, integrity, and legitimacy of sharing entities. Furthermore, blockchain has been integrated with knowledge-based networks for traffic optimization, resource sharing, network administration, access control, protecting privacy, traffic filtering, anomaly or intrusion detection, network virtualization, massive data analysis, edge and cloud computing, and data center networking. Despite the fact that many academics have employed the concept of blockchain in cognitive networks to achieve various objectives, we can also identify challenges such as high energy consumption, scalability issues, difficulty processing big data, etc. that act as barriers for integrating the two concepts together. Academicians have not yet reviewed blockchain-based network solutions in diverse application categories for diverse knowledge-defined networks in general, which consider knowledge generation and dissemination using various techniques such as machine learning, fuzzy logic, and meta-heuristics. Therefore, this article fills a void in the content of the literature by first reviewing the diverse existing blockchain-based applications in diverse knowledge-based networks, analyzing and comparing the existing works, describing the advantages and difficulties of using blockchain systems in KDN, and, finally, providing propositions based on identified challenges and then presenting prospects for the future.

A Multi-Task Classification Method for Application Traffic Classification Using Task Relationships

As IT technology advances, the number and types of applications, such as SNS, content, and shopping, have increased across various fields, leading to the emergence of complex and diverse application traffic. As a result, the demand for effective network operation, management, and analysis has increased. In particular, service or application traffic classification research is an important area of study in network management. Web services are composed of a combination of multiple applications, and one or more application traffic can be mixed within service traffic. However, most existing research only classifies application traffic by service unit, resulting in high misclassification rates and making detailed management impossible. To address this issue, this paper proposes three multitask learning methods for application traffic classification using the relationships among tasks composed of browsers, protocols, services, and application units. The proposed methods aim to improve classification performance under the assumption that there are relationships between tasks. Experimental results demonstrate that by utilizing relationships between various tasks, the proposed method can classify applications with 4.4%p higher accuracy. Furthermore, the proposed methods can provide network administrators with information about multiple perspectives with high confidence, and the generalized multitask methods are freely portable to other backbone networks.

PWAGAT: Potential Web attacker detection based on graph attention network

Attacker probing behavior detection is a notch in the current security defense system. Most cyber-attack detection research focuses on real-time payload interception and attack source tracing. However, the defense system cannot predict the attack behavior before the attack launches and cannot provide sufficient reaction and preparation time for the network administrators. Therefore, the current security system urgently needs to be improved by detecting cyber-attack precursors. We propose a potential Web attacker identification method based on a graph attention network (PWAGAT) by studying the features of the attacker’s behavior pattern before launching a Web attack. The core of PWAGAT is to identify the probing behavior of attackers and find those suspicious users with a high probability of carrying out Web attacks. The PWAGAT trains the embedding learning representation of each behavioral node from the Web attack behavior graph (WABG) through GAT and then uses the deep forest algorithm to train a classification model that recognizes probing behaviors. On the WAB-dataset provided by the Institute of Information Security of Sichuan University, the experiment proved that PWAGAT performed better than other graph learning methods in performing node embedding and classification of hacking behaviors. The results showed that identifying hacker probing behavior could help discover potential Web attackers, alerting defenders to assist in subsequent attack detection.

Analisa Quality of Services Pada Jaringan Sekolah Dengan Metode Queue Tree Studi Kasus: Lab Bahasa SMAN 47 Jakarta

Abstract - The use of the internet with a large number of users has problems with fair and equitable use of bandwidth. The use of the internet in the computer network of SMAN 47 Jakarta is a very large number of students and teachers. Computer networks in schools are provided not only to support the teaching and learning process but also to support the very high mobility of the learning community. Of the many teachers, staff and students at SMAN 47 Jakarta who are active internet users, some users download (using the Download manager), and some use it for browsing, running YouTube, Twitter, Instagram applications, and so on. . With limited bandwidth and no regulation of bandwidth usage, causing unfair and equitable distribution of bandwidth, bandwidth management is carried out using the simple queue method, as well as to make it easier for network administrators to configure. The success of bandwidth management is carried out through Quality of Services (QOS) with indicators: throughput, jitter, packet loss and delay. The value used as a barometer of QOS success is the THIPON index. From the test results of the proposed system made by the author, a case study in the Lab room, that the implementation of bandwidth management with the simple queue method resulted in a score of 4 THIPON indexes on all measured QoS indicators. Through this bandwidth management, network administrators can provide guarantees to users on the availability and fairness of bandwidth usage according to the user's priorities and needs. It is necessary to make a Standard Operational Procedure (SOP) regarding the bandwidth management mechanism so that bandwidth management can be carried out in accordance with the standards and carried out by whoever is the network administrator.

Network Automation

Purpose: The article "Network Automation in the Contemporary Economy" explores the concepts and methods of effective network management. The application stack, Jinja template engine, automation architecture, Nornir inventory management, application flow, logging, debugging, and live code testing are just a few of the subjects it covers. Network administrators are more important as digital technologies evolve quickly in order to maintain a safe and dependable network connection. The purpose of this article is to give network administrators the information and abilities they need to successfully traverse the intricacies of network management. It starts by going through the application stack and explaining the roles and relationships between each layer. The Ninja template engine is then described, along with an explanation of how its potent grammar makes network configuration management simple.
 Methodology: The research design employed in this study is a combination of qualitative and quantitative approaches. It involved an extensive literature review to gather existing knowledge on network automation and management practices. Additionally, empirical data was collected through surveys and interviews with network administrators to understand their experiences, challenges, and perspectives on network automation.
 Findings: The study found that network automation offers numerous benefits, including increased efficiency, reduced human errors, and enhanced network security. The application stack was identified as a critical component of network architecture, and its proper management can significantly impact network performance. The Jinja template engine proved to be an effective tool for simplifying network configuration tasks and promoting standardization across the network infrastructure.
 Recommendations: To policymakers, we recommend investing in training programs and resources to equip network administrators with the necessary skills to implement and manage network automation effectively. Developing clear guidelines and standards for network automation can also help organizations adopt automation practices seamlessly.
 Theory: The study was informed by the "Network Automation Theory," which posits that automating network management tasks can streamline operations, enhance reliability, and free up human resources for more strategic initiatives. The theory suggests that proper implementation of automation tools and frameworks can lead to a more agile and resilient network infrastructure. The validation of the theory was achieved through empirical data collected from network administrators and their experiences with network automation. The findings aligned with the propositions of the theory, confirming that network automation indeed brings significant benefits to organizations.
 Policy: For policymakers, we propose the formulation of a comprehensive policy framework that encourages the adoption of network automation technologies. The policy should focus on providing financial incentives for businesses to invest in automation tools, fostering partnerships between government and private sectors to promote knowledge exchange, and establishing regulatory guidelines to ensure network security and data privacy in automated environments.
 Practice: To network administrators and practitioners, we recommend staying updated with the latest advancements in network automation technologies and tools. Investing time in training and upskilling can help practitioners gain expertise in using automation frameworks like Nornir and Jinja template engine. Additionally, fostering a culture of continuous learning and experimentation within organizations can lead to successful implementations of network automation practices.

Design and Implementation of a Vlsm Simulator

Variable-Length Subnet Masks (VLSM), often referred to as "subnetting a subnet", is used to maximize addressing efficiency. The network administrator is able to use a long mask on networks with few hosts, and a short mask on subnets with many hosts. This addressing scheme allows growth and does not involve wasting addresses. VLSM gives a way of subnetting a network with minimal loses of IP addresses for a specific range. Unfortunately, the network administrator has to perform several mathematical steps (or use charts) to get the required results from VLSM. In this paper, a simple graph simulator is proposed (using Visual Basic 6.0 Language) to perform all the required mathematical steps and to display the obtained required information (the subnet ID, broadcast ID, usable addresses for sub networks and others). The simulator also includes the ability to draw a suggested network topology that matches the entries. The implementation of the simulation required only very few entries (IP, prefix and number of subnets).This simulator is useful for students, instructors, and network engineers to analysis and design a VLSM network by providing all required information in simple, fast and easy steps. Moreover, the software draws a full detailed suggested network topology which is considered a helpful tool for the network administrator that he should have.