Network Infrastructure Research Articles

Integrating Artificial Intelligence in Cybersecurity Detection and Response

The article presents the possibilities of using artificial intelligence in the context of cybersecurity. It outlines the role of artificial intelligence in the face of increasing threats to network infrastructure. Section 1 discusses the justification for using artificial intelligence in cybersecurity, while Sections 2 and 3 explore the concept of detecting and preventing incidents using incident response automation. Meanwhile, Section 4 is dedicated to identity and access management in the context of accessing organizational resources.

Discrete Event Modeling and Simulation Approaches for IIoT

The industry has experienced significant advancements in recent years, primarily focusing on smart manufacturing, culminating in the Industry 4.0 (I4.0) revolution I4.0 emphasizes interconnectivity, real time data capture and transmission among machines, autonomy, and machine learning, providing manufacturing companies numerous growth opportunities. The Industrial Internet of Things (IIoT) is a core component of this revolution, becoming integral to each system and increasing complexity due to the vast number of interconnected devices and diverse physical components. The variety of virtual services distributed across the architectural layers of industrial systems (cloud, fog, edge) and the various connection types between IIoT devices introduce security and privacy challenges, which are critical issues for any system incorporating IIoT. To fully leverage IIoT’s potential, addressing these security and privacy concerns is essential. Research and design in this domain are challenging, particularly when creating a simulation environment to study a system’s behavior over time. Despite the extensive research in IoT and the significant benefits of simulation based approaches, there remains a challenge in creating detailed representations from the underlying IoT nodes to the application layer in the cloud, along with the underlying networking infrastructure. To assist researchers and practitioners in overcoming these challenges, we propose the Discrete Event System Specification (DEVS) formalism. DEVS provides a mathematical framework for modeling systems, whether discrete or continuous events, allowing for the simulation of these systems within the DEVS environment. Every system, whether real or conceptual, has a time base, inputs, outputs, and functions to determine the next state, as well as outputs that reflect the current state and inputs. Simulating the system within the DEVS environment allows one to study its behavior to predict and optimize performance patterns.

AI in Network Infrastructure: Transforming Telecommunications with Intelligent Systems

AI technologies are drastically changing the backbone of telecommunications networks and bringing unheard-of security, network management, and optimization capabilities. From dynamic resource allocation to predictive maintenance and intelligent traffic management, this article shows how artificial intelligence and machine learning technologies are being used to solve important problems in modern telecommunications. By means of a thorough investigation of AI-driven solutions, this article shows that technologies are offering real-time network optimization, automatic threat identification, and improved service dependability while drastically lowering operating costs. While addressing important technical issues and operational considerations, the conversation covers pragmatic solutions across several network domains, including 5G infrastructure, edge computing, and autonomous networking systems. Examining real-world case studies and developing trends helps this article show how artificial intelligence is not only transforming present network operations but also opening the path for future telecommunications innovations, enabling more resilient, efficient, and intelligent network infrastructure that can adapt to changing technological demand and user expectations.

Fiber To The Home (Ftth) Network Infrastructure Design Using Gigabyte Passive Optical Network (Gpon) Technology South Solok District

Fiber To The Home (FTTH) Network with Gigabit Passive Optical Network (GPON) Technology in South Solok Regency, supervised by Ibu Amelia Yolanda, ST as supervisor 1 and Ibuk Popy Maria, ST STT as supervisor 2. Development of Telecommunications technology as a necessity Currently, communication has developed rapidly, transmission media facilities are needed that are capable of transmitting information with large capacities and adequate data transfer speeds. By implementing GPON technology, FTTH services can reach users at up to speed. This research aims to design an FTTH network using GPON technology in the South Solok Regency area, obtain results of identification and analysis of communication network needs for the Nagari Lubuk Gadang Selatan community in South Solok Regency, obtain results of FTTH network infrastructure design using GPON technology for Nagari Lubuk Gadang Selatan Regency. South Solok.

Can network infrastructure construction alleviate individual depression? Evidence from a quasi-natural experiment of “Broadband China”

Can network infrastructure construction alleviate individual depression? Evidence from a quasi-natural experiment of “Broadband China”

The better bandit: decentralised infrastructure, crypto-States, and the rematerialisation of virtual worlds

ABSTRACT This paper examines the role of hardware security as the basis for order in the decentralised metaverse. It does this by considering the infrastructural tools and governance practices at the heart of KONG Land, an example of a blockchain-based decentralised autonomous organisation (DAO) and decentralised physical infrastructure network (DePIN) project. KONG Land manufactures open-source microchips to create verifiable hardware that anyone can use or integrate into their own application. KONG Land’s focus on the materiality of infrastructure led them to pursue a governance model as a digital-physical, politically decentralised polity. By foregrounding the physicality and affordances of decentralised efforts to manufacture microchips, this paper shows how rematerialising digital domains leads back to questions of statehood and its purpose and provides an explanation for emerging sovereignties. Building on Olson’s (1993. Dictatorship, democracy, and development. American Political Science Review, 87(3), 567–576) theory of the stationary bandit, the paper positions projects like KONG Land as an attempt to create a ‘better bandit’ – one that sets out to provide its citizens with a superior level of security than that offered by either nation states or the corporate metaverse, with the intention of creating the conditions for Web3 production and expansion.

Energy-Efficient Green Information Centric Networking for Future Wireless Communications

Energy-efficient green information-centric networking (EEGICN) is proposed in this paper for advancing future wireless communication networks by addressing the challenge of energy consumption. This model can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The model aimed to reduce energy consumption and increase network performance and stability. The EEGICN model incorporates efficient routing mechanisms, content caching strategies, and energy-intelligent communication protocols to improve resource utilization across the network infrastructure. EEGICN minimizes large data transmission, reduces power consumption, and reduces network congestion. Popular resources are cached in key network locations, and proximity data is exchanged to achieve this. The model also includes dynamic power management algorithms that adapt to changing traffic demand and network conditions to provide consistent performance across a variety of operational scenarios. In comparison to current wireless network systems that employ various forms of cache, the evaluation findings demonstrated that EEGICN can increase network efficiency by dramatically lowering the number of hops and energy consumption. Future networks may find this application to be a quick and easy way to transmit content.

Fault Tolerance Management Implementation from Medium-to-Large-Scale Networks

As network infrastructures grow in complexity, ensuring high availability and resilience becomes critical, especially for medium-to-large scale networks. This study focuses on the development and implementation of fault tolerance management within Software-defined networking (SDN) environments, aimed at minimizing downtime and enhancing network reliability. SDN’s centralized control and dynamic programmability provide an ideal framework for implementing efficient fault detection and recovery mechanisms. The proposed model leverages real-time monitoring, redundancy protocols, and adaptive rerouting strategies to mitigate the impact of node or link failures. Key components of the model include failover mechanisms, load balancing, and traffic rerouting algorithms, designed to maintain seamless network operations during failures. Through simulation and testing, the model demonstrates significant improvements in network resilience, reducing recovery time and ensuring uninterrupted service delivery. This research provides a comprehensive guide to implementing fault-tolerant networks using SDN, offering scalable solutions that can be adapted to various network sizes and configurations. The findings emphasize the potential of SDN to revolutionize fault management in modern network infrastructures, making it a crucial consideration for future network design and operations.

Enhancing Intrusion Detection Systems with Dimensionality Reduction and Multi-Stacking Ensemble Techniques

The deployment of intrusion detection systems (IDSs) is essential for protecting network resources and infrastructure against malicious threats. Despite the wide use of various machine learning methods in IDSs, such systems often struggle to achieve optimal performance. The key challenges include the curse of dimensionality, which significantly impacts IDS efficacy, and the limited effectiveness of singular learning classifiers in handling complex, imbalanced, and multi-categorical traffic datasets. To overcome these limitations, this paper presents an innovative approach that integrates dimensionality reduction and stacking ensemble techniques. We employ the LogitBoost algorithm with XGBRegressor for feature selection, complemented by a Residual Network (ResNet) deep learning model for feature extraction. Furthermore, we introduce multi-stacking ensemble (MSE), a novel ensemble method, to enhance attack prediction capabilities. The evaluation on benchmark datasets such as CICIDS2017 and UNSW-NB15 demonstrates that our IDS surpasses current models across various performance metrics.

Barriers and recommendations for the implementation of robot-assisted minimally invasive surgery in Africa.

Robotic surgery offers several advantages to the African setting, including shorter hospital stays, faster return to work, and increased overall productivity. However, its adoption has been limited by several factors. This review aims to present the barriers to implementation, and recommendations for integrating robotic surgery into the African healthcare system. Use of robotic surgery in Africa is primarily limited to Egypt and South Africa. Barriers faced by other countries were categorized into economic, infrastructural, systemic, and training-related. They include limited healthcare budgets, initial costs of robotic systems, patients' inability to afford robotic procedures, out-of-pocket healthcare financing, inadequate power supply, limited internet connectivity, poor healthcare leadership, and insufficient surgeon training facilities. Public-private partnerships, provision of loans and subsidies, introduction of cheaper robotic systems, and local manufacturing of robotic equipment will serve as cost-effective innovations. It is also important to improve healthcare financing and strengthen healthcare leadership across Africa. To address the lack of surgeon training facilities, remote assistance for surgeon training can be used to create a mentor-mentee relationship between robotic surgeons in any part of the world and surgical trainees in Africa to facilitate knowledge transfer. Prior investment in electricity and network infrastructure is however necessary. Establishment of fellowships to provide early exposure to robotic surgery should also be explored. AI-integrated robotic surgery can also enhance precision and safety, and provide tailored training tools for surgeons. Similar barriers to the adoption of surgical robotics are faced across Africa. By implementing the provided recommendations, robotic surgery can still be widely adopted in African settings, despite the delay.

Decentralized Payment Framework for Low-Connectivity Areas Using Ethereum Blockchains

This paper presents a pioneering analytical framework for a secure payment system leveraging blockchain technology tailored to regions with suboptimal network connectivity. Contemporary payment mechanisms utilizing Ethereum are predominantly optimized for areas with robust network infrastructure, neglecting regions with less connectivity. To address this gap, the proposed model integrates novel security attributes and employs an analytical method to design a decentralized payment system. The framework facilitates communication between low-connectivity zones and Internet service providers through auxiliary nodes, creating a local blockchain network for residents, merchants, and auditors. A mathematical model quantifies operational costs, transaction processing, and synchronization of auxiliary nodes, ensuring a resilient and secure payment architecture. A unique aspect of the proposed approach is its robustness against auditor outages and network variability, coupled with an empirical analysis of incentive structures for auditors' block validation activities. Moreover, it delineates the minimum requirements for secure transaction completion. Empirical findings showed a significant improvement in system efficiency, including a 79% reduction in block time, a 28% increase in transaction throughput, a 30% decrease in energy consumption, a 68% shorter confirmation time, a 63% reduction in execution time, a 46% increase in block production rate, and 82% reduced network variability. This study's significant contribution lies in introducing a sustainable, cost-effective, and secure payment system for regions with inadequate network services.

A novel deep anomaly detection approach for intrusion detection in futurisitic network

<span lang="EN-US">In an era where networks are increasingly heterogeneous and multi-domain, establishing robust security models to protect data and network infrastructure is becoming ever more complex. Traditional intrusion detection systems (IDS) often struggle with novel or variant attacks that fall outside predefined rule sets, resulting in significant detection challenges. This paper proposes a methodologically refined approach leveraging data-driven insights and statistically robust feature selection to enhance the training dataset. The study presents a long short-term memory-autoencoder (LSTM-AE) based learning model designed for multi-class anomaly detection. The model's novelty lies in its application of distance metrics to define distinct thresholds for varied attack classifications, a strategy that significantly amplifies detection precision. Experimental results validate the superior performance of the proposed system, achieving 94.82% accuracy rate, outperforming similar existing works. The study also proactively addresses common issues of class imbalance and skewed data representation in benchmark datasets by strategically training the model on normal traffic, enhancing its capability to generalize and identify anomalies effectively.</span>

Multiyear Maintenance and Rehabilitation Optimization for Large-Scale Infrastructure Networks: An Enhanced Genetic Algorithm Approach

Multiyear Maintenance and Rehabilitation Optimization for Large-Scale Infrastructure Networks: An Enhanced Genetic Algorithm Approach

Application of hybrid strategies of complex network attack and defense games

Critical infrastructure networks serve as the backbone of modern society’s operations, and the application of game theory to safeguard these networks against deliberate attacks under resource constraints is of paramount importance. Regrettably, the existing body of research on hybrid attack and defense strategies for nodes and edges is notably scarce, despite the ubiquity of such strategies in practical contexts. Current understanding regarding the establishment of a cost constraint model within the framework of hybrid attack and defense strategies, the strategic inclinations of both adversarial and defensive parties under varying cost constraint coefficients, and the influence of resource allocation ratios on equilibrium outcomes remains limited. Consequently, this study constructs a game-theoretic model for the engagement of critical infrastructure networks under non-uniform cost constraints, incorporating hybrid attack and defense strategies for nodes and edges, and conducts an equilibrium analysis across a range of cost constraint coefficients and resource allocation ratios. The findings reveal that when resource availability is limited, both attackers and defenders are inclined towards a strategy of concentrated resource allocation; in contrast, under most circumstances, defenders exhibit a preference for an equitable distribution of resources.

Assessing the vulnerability of empirical infrastructure networks to natural catastrophes

Human-made infrastructures are complex systems continually exposed to events that threat their function, such as cascading failures, occurring when the flow of physical quantities is redistributed within the network as a consequence of localized disruptions. Nevertheless, the role played by exogenous catastrophic events and internal failures on the robustness of critical infrastructures is usually addressed independently, under simplifying assumptions and lacking a unified picture for realistic risk assessments. Here, we fill this gap by introducing the Operational-Affected-Dismantled (OAD) model that captures both local and nonlocal failure propagation mechanisms. The model combines reaction–diffusion processes for local spreading with a global field effect for long-range interactions, allowing us to quantitatively characterize the cascade dynamics in infrastructure networks. Moreover, we include information on external stressors to assess the robustness of empirical network infrastructures and build spatial risk maps. By using data from severe storms (2009–2016) and from earthquakes (2000–2023) as stressors of the North American power grid and the worldwide airline transportation system, respectively, we offer a quantitative way to rank events by their potential to trigger systemic effects. By analyzing the response of the European power grid to simulated severe storms, we find that it can show high levels of systemic risk. Uncertainty in global climate and the accelerating frequency of extreme events all over the globe call for novel strategies to quantify, adapt to and mitigate systemic risk. Our framework provides a suitable starting point to assess the robustness of empirical systems in realistic and what-if scenarios.

EVALUATING THE EFFECTIVENESS OF CUSTOMER COMPLAINT RESPONSES ON NMB MKONONI SATISFACTION: A CASE STUDY OF NMB BANK PLC IN DODOMA CITY, TANZANIA

This research examines the efficiency of customer complaint responses and their impact on satisfaction with NMB Mkononi services in Tanzania, specifically focusing on NMB Bank PLC in Dodoma City. The study aims to evaluate how effectively NMB handles customer complaints related to NMB Mkononi services. A sample of 124 respondents, including NMB Mkononi customers, staff, and branch managers, participated in the study. Data were gathered through questionnaires, interviews, and document reviews, and analyzed using both qualitative and quantitative methods. Findings indicated moderate usage of NMB Mkononi services in Dodoma City and effective responses to customer complaints by NMB Bank (total mean = 3.56). The study concludes that NMB must consistently deliver high-quality services to customers and recommends enhanced investment in reliable network infrastructure, ICT expertise, facilities, and technological adaptation to boost customer satisfaction and profitability.

Expanding metaverse market: New opportunities and challenges for the content industry

<p>The metaverse is expanding from a simple technology or service into a platform-centric industrial ecosystem. Initially discussed in the context of infrastructure networks, such as computer communication networks, it has since broadened to include content and services related to virtual reality, such as games and films. Recently, the scope of this expanded ecosystem has further widened, linking various types of content through content-based platforms. In this metaverse market, the content market plays a crucial role. This paper aims to examine the current state of the content market within the metaverse market and explore its future development directions. We will analyze various aspects of the content sector utilizing the metaverse both domestically and internationally, investigating how the use of the metaverse is evolving from mere entertainment to a productive market with applicable business models.</p>

Software Define Wide Area Network (SDWAN) network optimization analysis on radiolink-fiber optic access media migration

The development of technology in radiolink networks has grown rapidly and is suitable to be implemented on wide coverage area networks, but weather and physical environment factors greatly affect signal conditions. The background that has been stated, this study proposes migration to fiber optic access media because it is able to send data faster and is not affected by electromagnetic interference and is stable. Furthermore, this study conducted an optimization analysis on the SDWAN (Software Defined Wide Area Network) network, SDWAN functions as a firewall that can provide connectivity support and infrastructure network development in branch offices with system security in one centralized platform. The test method used in this study is a connectivity test from each remote fortigate located in the branch office. While the analysis method used is Quality of Service (QoS), which is a method to find out data delivery (Packet Loss, Delay and Jitter) using the FortiManager application, which is the analytical and experimental proof of concept of the SDWAN network model using the BGP method for auto failover testing.

A graph-empowered agent-based simulation: Impacts of coordination schemes on critical infrastructures resilience

Critical infrastructure systems are governed by several sectors working together to maintain social, economic, and environmental well-being. Their cyber–physical interdependencies influence their performance and resilience to routine failures and extreme events. To balance investment and restoration decisions in the face of disruptive events, mostly centralized mathematical formulations and solutions were presented in the literature. However, not all physical and dynamic characteristics of infrastructure systems and their decision makers can be modeled via mathematical models. In this study, we take a different approach and utilize agent-based modeling to simulate city-scale interdependent infrastructure networks as a complex adaptive system. In specific, we design a flexible modular (object-oriented) simulation tool capable of capturing the dynamic behaviors of various networks. We first model each infrastructure as a weighted graph with relevant geospatial attributes. Decision makers for each infrastructure sector are represented by intelligent agents. We then define three information and coordination schemes among agents, including no communication, leader–follower, and decentralized coalitions. To show the applicability of the approach, we use publicly available interdependent water distribution and road networks for the City of Tampa, FL, which is prone to hurricanes. We simulate different magnitudes of physical, cyber, and cyber–physical failures, evaluate resource allocation decisions, made by agents under each coordination scheme, and quantify the aggregated resilience. The simulation platform will help municipalities in various cities to quantify the impact of their collective decision making and identify the best coordination structures when interdependencies are modeled in infrastructure systems.

Implementation of Cyber Network’s Attacks Detection System with Deep Learning Designing Algorithms

The internet has become indispensable for modern communication, playing a vital role in the development of smart cities and communities. However, its effectiveness is contingent upon its security and resilience against interruptions. Intrusions, defined as unauthorized activities that compromise system integrity, pose a significant threat. These intrusions can be broadly categorized into host intrusions, which involve unauthorized access and manipulation of data within a system, and network intrusions, which target vulnerabilities within the network infrastructure. To mitigate these threats, system administrators rely on Network Intrusion Detection Systems (NIDS) to identify and respond to security breaches. However, designing an effective and adaptable NIDS capable of handling novel and evolving attack strategies presents a significant challenge. This paper proposes a deep learning-based approach for NIDS development, leveraging Self-Taught Learning (STL) and the NSL-KDD benchmark dataset for network intrusion detection. The proposed approach is evaluated using established metrics, including accuracy, F-measure, recall, and precision. Experimental results demonstrate the effectiveness of STL in the 5-class categorization, achieving an accuracy of 79.10% and an F-measure of 75.76%. This performance surpasses that of Softmax Regression (SMR), which attained 75.23% accuracy and a 72.14% F-measure. The paper concludes by comparing the proposed approach's performance with existing state-of-the-art methods.