Network Users Research Articles

Exploring the Impact of Resource Management Strategies on Simulated Edge Cloud Performance: An Experimental Study

Edge computing has emerged as a critical technology for meeting the needs of latency-sensitive applications and reducing network congestion. This goal is achieved mainly by distributing computational resources closer to end users and away from traditional data centers. Optimizing the utilization of limited edge cloud resources and improving the performance of edge computing systems requires efficient resource-management techniques. In this paper, we primarily discuss the use of simulation tools—EdgeSimPy in particular—to assess edge cloud resource management methods. We give a summary of the main difficulties in managing a limited pool of resources in edge cloud computing, and we go over how simulation programs like EdgeSimPy work and evaluate resource management algorithms. The scenarios we consider for this evaluation involve edge computing while taking into account variables like user location, resource availability, and network structure. We evaluate four resource management algorithms in a fixed, simulated edge computing environment to determine their performance regarding their CPU usage, memory usage, disk usage, power consumption, and latency performance metrics to determine which method performs better in a fixed scenario. This allows us to determine the optimal algorithm for tasks that prioritize minimal resource use, low latency, or a combination of the two. Furthermore, we outline areas of unfilled research needs and potential paths forward for improving the reliability and realism of edge cloud simulation tools.

STRUCTURAL AND FUNCTIONAL ASPECTS OF POLITICAL COMMUNICATION IN THE CONTEXT OF MODERN SOCIAL DEVELOPMENT

The article highlights the structural and functional features of political communication as a process of communicative interaction in the political sphere in the conditions of modern social development. It was determined that political communication is a communicative process of mutual exchange of political information and broadcasting of political discourse among political subjects, state administration bodies, civil society, aimed at achieving consensus in making political and managerial decisions, legitimizing power and maintaining the stability of the democratic political system through means mass communication, Internet social networks, informal contacts, etc. The leading functions of political communication in terms of its structural elements are distinguished: 1) subjects of political communication: citizens, state authorities and management, political parties, movements and associations, civil society institutions, associations and groups of users of social networks on the Internet etc; 2) feedback between the subjects of political communication, acting as a communicative interaction that takes on different forms of information exchange; 3) information message as a relevant political discourse; 4) channels and means of information transmission and two-way communication between subjects of political communication.

DETECCIÓN AUTOMÁTICA DE MISOGINIA EN X USANDO INTELIGENCIA ARTIFICIAL

Nowadays, the number of social network users has grown exponentially. One example of these social networks is X, which plays an important role in the generation and dissemination of information. However, this increase in the use of social networks has also led to an increase in the dissemination of misogynistic messages.

Received Power Problems in 3G and their Impact on Almadar Aljaded Network Performance

Abstract: In Libya, there are two types of mobile phone networks (Libyana and Almadar Aljaded), and with the development and passage of time, it has become necessary to keep pace with technology smoothly, and on the other hand, the number of network users is increasing, which causes problems that affect the performance of the networks. This study dealt with one of the networks, which is the Almadar Aljaded network for communications and the energy received in the third generation uplink, indicating its values and the factors affecting the studied network, with the development of an algorithm to address the problems that may arise, and this algorithm has proven its effectiveness in addressing three different cases.

An enhanced whale optimization algorithm for task scheduling in edge computing environments

The widespread use of mobile devices and compute-intensive applications has increased the connection of smart devices to networks, generating significant data. Real-time execution faces challenges due to limited resources and demanding applications in edge computing environments. To address these challenges, an enhanced whale optimization algorithm (EWOA) was proposed for task scheduling. A multi-objective model based on CPU, memory, time, and resource utilization was developed. The model was transformed into a whale optimization problem, incorporating chaotic mapping to initialize populations and prevent premature convergence. A nonlinear convergence factor was introduced to balance local and global search. The algorithm's performance was evaluated in an experimental edge computing environment and compared with ODTS, WOA, HWACO, and CATSA algorithms. Experimental results demonstrated that EWOA reduced costs by 29.22%, decreased completion time by 17.04%, and improved node resource utilization by 9.5%. While EWOA offers significant advantages, limitations include the lack of consideration for potential network delays and user mobility. Future research will focus on fault-tolerant scheduling techniques to address dynamic user needs and improve service robustness and quality.

Real-time fake news detection in online social networks: FANDC Cloud-based system

Social networks have become a common way for people to communicate with each other and share ideas, thanks to their fast information-sharing features. But fake news spread on social networks can cause many negative consequences by affecting people’s daily lives. However, the literature lacks online and real-time fake news detection systems. This study aims to fill this gap in the literature and to handle the fake news detection problem with a system called FANDC, based on cloud computing, to cope with fake news in seven different categories, and to solve the real-time fake news detection problems. The system was developed using the CRISP-DM methodology with a hybrid approach. BERT algorithm was used in the system running on the cloud to avoid possible cyber threats with the dataset created with approximately 99 million big data from COVID-19-TweetIDs GitHub repository. It was trained in two periods with 100% accuracy during the modeling phase in terms of training accuracy. Experimental results of the FANDC system performed the real-time detection of fake news at 99% accuracy. However, previous studies experimental level success rate in the literature, were around 90%. We hope that the developed system will greatly assist social network users in detecting fake news in real-time.

The Influence Of Originality, Quality, And Quantity On, Intention To Follow The Advice, Through Hedonic Experience, Opinion Leadership, And Perceived Aesthetics

Factors that determine the success of influencer marketing focusing on TikTok social network users This study examines the influence of influencer account characteristics (Perceived Originality Perceived Quantity, Perceived Quality), through Hedonic Experience, Opinion Leadership and Perceived Aesthetics and how this impacts their intention to Intention to Follow The Advice, if users feel that the content presented by an influencer is originality and has a large variety of content, they will tend to view the influencer as opinion leadership and, as a result are likely to follow the advice and recommendations of the influencer. The purpose of this study is to identify the determinants of influencer marketing success on TikTok and identify the characteristics that are most effective in attracting users on TikTok. This study was designed using purposive sampling method involving TikToker users and followers across Indonesia. quantitative research using partial least squares path modeling method (PLS-SEM). Some of the findings in this study are that there is no significant relationship between perceived aesthetics and intention to follow the advice and perceived originality has an indirect effect on opinion leadership. The managerial implication of this research for industry practitioners and researchers is that companies can design more targeted and effective marketing campaigns by understanding how perceived originality, quantity, and quality factors affect intention to follow the advice through hedonic experience. A thorough analysis with user preferences and an opportunity to build stronger and sustainable partnerships with the company's influencers and expand the reach of their campaigns, increasing the company's brand awareness.

Ingenuidade e Inércia Cognitiva como Atributos para a Viralização de Fake News nas Redes Sociais Online

The dissemination of fake news on online social networks has created a scenario of disinformation in which manipulation and control are used to influence behavior and gain advantages. This phenomenon has motivated joint efforts to inhibit its spread. However, despite these initiatives, the problem persists. The assumption is that users of these channels, who are key agents in confronting this phenomenon, become contributors to disinformation when affected by naivety or cognitive inertia. The aim is to analyze the role of these users in the spread of fake news and identify factors that influence their uncritical stance. Conducted through a narrative review of the literature for theoretical purposes, this analysis adopts a reflective, exploratory and essayistic approach to the condition of co-participants of online social network users in the face of the problem of fake news, based on key themes such as post-truth and critical information competence, and illustrated by an empirical analysis of three specific cases of viral fake news. It is concluded that users of online social networks, when naive and inert, are part of the problem and, therefore, need to be more reactive, instead of, consciously or unconsciously, becoming agents of misinformation.

A Deep Learning-Based Car Accident Detection Framework Using Edge and Cloud Computing

The evolving technological landscape has seen a pivotal shift with the advent of edge computing, transforming various sectors, particularly accident detection. Edge computing enhances road safety by enabling realtime data processing from onboard sensors, cameras, and connected devices, addressing limitations in traditional cloudbased systems. This paper introduces a deep learning-based accident detection framework within an edge-cloud setup. Utilizing a CNN model, accidents are detected at the edge node near the data source, ensuring low latency, reduced network usage, and faster execution times. The model achieves a remarkable 95.91% accuracy.

MEGA: Maximum-Entropy Genetic Algorithm for Router Nodes Placement in Wireless Mesh Networks

Over the past decade, wireless mesh networks (WMNs) have seen significant advancements due to their simple deployment, cost-effectiveness, ease of implementation, and reliable service coverage. However, despite these advantages, the placement of nodes in WMNs presents a critical challenge that significantly impacts their performance. This issue is recognized as an NP-hard problem, underscoring the necessity of development optimization algorithms, such as heuristic and metaheuristic approaches. This motivated us to develop the Maximum Entropy Genetic Algorithm (MEGA) to address the issue of mesh router node placement in WMNs. To assess the proposed method, we conducted experiments across various scenarios with different settings, focusing on key metrics such as network connectivity and user coverage. The simulation results showed the comparative performance of MEGA in relation to other prominent algorithms, such as the Coyote Optimization Algorithm (COA), Firefly Algorithm (FA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), revealing MEGA’s effectiveness and usability in determining optimal locations for mesh routers.

An Efficient Deep Learning Mechanism for Predicting Fake News/Reviews in Twitter Data

Recently, social media platforms have been widely utilized as information sources due to their effortless accessibility and reduced costs. However, online platforms like Instagram, Twitter and Facebook get influenced by their users via fake news/reviews. The main intention of spreading fake news is to mislead other network users, which highly affects businesses, political parties, etc. Thus, an effective methodology is needed to predict fake news from social media automatically. The major objective of this proposed study is to identify and classify the given Twitter input data as real or fake through deep learning mechanisms. The proposed study involves four stages: pre-processing, embedded word analysis, feature extraction, and fake news/reviews prediction. Initially, pre-processing is performed to enhance the quality of data with the help of tokenization, stemming and stop word removal. Embedded word analysis is done using Advanced Word2Vec and GloVe modeling to enhance the performance of a proposed prediction model. Then, the hybrid deep learning model named Dense Convolutional assisted Gannet Optimal Bi-directional Network (DC_GO_BiNet) is introduced for feature extraction and prediction. A Dense Convolutional Neural Network (DCNN) is hybridized with a bi-directional long-short-term memory (Bi-LSTM) model to extract the essential features and predict fake news from the given input text. Also, the proposed model’s parameters are fine-tuned by adopting a gannet optimization (GO) algorithm. The proposed study used three different datasets and obtained higher classification accuracy as 99.5% in Fake News Detection on Twitter EDA, 99.59% in FakeNewsNet and 99.51% in ISOT. The analysis proves that the proposed model attains higher prediction results for each dataset than others.

Continuous-variable quantum passive optical network

To establish a scalable and secure quantum network, a critical milestone is advancing from basic point-to-point quantum key distribution (QKD) systems to the development of inherently multi-user protocols designed to maximize network capacity. Here, we propose a quantum passive optical network (QPON) protocol based on continuous-variable (CV) systems, particularly the quadrature of the coherent state, which enables deterministic, simultaneous, and high-rate secret key generation among all network users. We implement two protocols with different trust levels assigned to the network users and experimentally demonstrate key generation in a quantum access network with 8 users, each with an 11 km span of access link. Depending on the trust assumptions about the users, we reach 1.5 and 2.1 Mbits/s of total network key generation (or 0.4 and 1.0 Mbits/s with finite-size channels estimation). Demonstrating the potential to expand the network’s capacity to accommodate tens of users at a high rate, our CV-QPON protocols open up new possibilities in establishing low-cost, high-rate, and scalable secure quantum access networks serving as a stepping stone towards a quantum internet.

Uncovering Hidden Risks in IoT devices: A Post-Pandemic National Study of SOHO Wi-Fi Router Security

This study thoroughly analyzes the cybersecurity status of Small Office/Home Office (SOHO) Wi-Fi routers. These routers are crucial but frequently overlooked elements in network infrastructure, particularly in light of the impact of the COVID-19 pandemic on network security. The pandemic has led to shifts in network usage patterns, blurring traditional security perimeters and extending them into private residences, creating additional points of vulnerability in urban environments. Our nationwide research evaluated an extensive dataset of router brands and models currently used at scale. We measured the prevalence of known vulnerabilities, assessed the currency of userspace and kernel software versions, and compared the security robustness of proprietary firmware against open-source alternatives. Our findings reveal a concerning landscape of widespread vulnerabilities and outdated software components, posing latent risks to end-users. The results indicate a predominance of Linux on MIPS and ARM architectures, with an average delay of 5 to 10 years between the release of the kernel and the implementation of the most recent firmware versions. As a result, we observed an average of 1344 and 72 vulnerabilities in the kernel and applications. One significant discovery from our research is that replacing the manufacturer's original firmware with open-source alternatives, such as DD-WRT, OpenWrt, and Tomato, can substantially enhance the security of the software stack. This enhancement results in improvements of up to 97% in the case of binaries and 98.42% in the kernel. Our research helps increase cybersecurity awareness by pinpointing critical home network environment weaknesses and alerting the need for more rigorous security practices in producing and maintaining SOHO routers. This investigation also allowed the report of a new remote code execution vulnerability (disclosed in CVE-2022-46552).

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

Challenges and Advances in Analyzing TLS 1.3-Encrypted Traffic: A Comprehensive Survey

The widespread adoption of encrypted communication protocols has significantly enhanced network security and user privacy, simultaneously elevating the importance of encrypted traffic analysis across various domains, including network anomaly detection. The Transport Layer Security (TLS) 1.3 protocol, introduced in 2018, has gained rapid popularity due to its enhanced security features and improved performance. However, TLS 1.3’s security enhancements, such as encrypting more of the handshake process, present unprecedented challenges for encrypted traffic analysis, rendering traditional methods designed for TLS 1.2 and earlier versions ineffective and necessitating the development of novel analytical techniques. This comprehensive survey provides a thorough review of the latest advancements in TLS 1.3 traffic analysis. First, we examine the impact of TLS 1.3’s new features, including Encrypted ClientHello (ECH), 0-RTT session resumption, and Perfect Forward Secrecy (PFS), on existing traffic analysis techniques. We then present a systematic overview of state-of-the-art methods for analyzing TLS 1.3 traffic, encompassing middlebox-based interception, searchable encryption, and machine learning-based approaches. For each method, we provide a critical analysis of its advantages, limitations, and applicable scenarios. Furthermore, we compile and review key datasets utilized in machine learning-based TLS 1.3 traffic analysis research. Finally, we discuss the main challenges and potential future research directions for TLS 1.3 traffic analysis. Given that TLS 1.3 is still in the early stages of widespread deployment, research in this field remains nascent. This survey aims to provide researchers and practitioners with a comprehensive reference, facilitating the development of more effective TLS 1.3 traffic analysis techniques that balance network security requirements with user privacy protection.

Multi-Agent DRL for Air-to-Ground Communication Planning in UAV-Enabled IoT Networks.

In this paper, we present a novel method to enhance the sum-rate effectiveness in full-duplex unmanned aerial vehicle (UAV)-assisted communication networks. Existing approaches often couple uplink and downlink associations, resulting in suboptimal performance, particularly in dynamic environments where user demands and network conditions are unpredictable. To overcome these limitations, we propose a decoupling of uplink and downlink associations for ground-based users (GBUs), significantly improving network efficiency. We formulate a comprehensive optimization problem that integrates UAV trajectory design and user association, aiming to maximize the overall sum-rate efficiency of the network. Due to the problem's non-convexity, we reformulate it as a Partially Observable Markov Decision Process (POMDP), enabling UAVs to make real-time decisions based on local observations without requiring complete global information. Our framework employs multi-agent deep reinforcement learning (MADRL), specifically the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, which balances centralized training with distributed execution. This allows UAVs to efficiently learn optimal user associations and trajectory controls while dynamically adapting to local conditions. The proposed solution is particularly suited for critical applications such as disaster response and search and rescue missions, highlighting the practical significance of utilizing UAVs for rapid network deployment in emergencies. By addressing the limitations of existing centralized and distributed solutions, our hybrid model combines the benefits of centralized training with the adaptability of distributed inference, ensuring optimal UAV operations in real-time scenarios.

Leveraging blockchain and federated learning in Edge-Fog-Cloud computing environments for intelligent decision-making with ECG data in IoT

Blockchain technology combined with Federated Learning (FL) offers a promising solution for enhancing privacy, security, and efficiency in medical IoT applications across edge, fog, and cloud computing environments. This approach enables multiple medical IoT devices at the network edge to collaboratively train a global machine learning model without sharing raw data, addressing privacy concerns associated with centralized data storage. This paper presents a blockchain and FL-based Smart Decision Making framework for ECG data in microservice-based IoT medical applications. Leveraging edge/fog computing for real-time critical applications, the framework implements a FL model across edge, fog, and cloud layers. Evaluation criteria including energy consumption, latency, execution time, cost, and network usage show that edge-based deployment outperforms fog and cloud, with significant advantages in energy consumption (0.1% vs. Fog, 0.9% vs. Cloud), network usage (1.1% vs. Fog, 31% vs. Cloud), cost (3% vs. Fog, 20% vs. Cloud), execution time (16% vs. Fog, 28% vs. Cloud), and latency (1% vs. Fog, 79% vs. Cloud).

Defending Against SSDF Attacks From Randomly Appearing Intelligent Malicious Vehicle Users in the CIoV Network by Bayesian Stackelberg Game

Defending Against SSDF Attacks From Randomly Appearing Intelligent Malicious Vehicle Users in the CIoV Network by Bayesian Stackelberg Game

Dual Graph Neural Networks for Dynamic Users’ Behavior Prediction on Social Networking Services

Dual Graph Neural Networks for Dynamic Users’ Behavior Prediction on Social Networking Services

The influence maximization algorithm for integrating attribute graph clustering and heterogeneous graph transformer

In social networks, maximizing influence is an important research direction. However, traditional influence maximization algorithms often overlook the attribute information of nodes and the heterogeneity of networks, leading to inefficiency and inaccuracy in the propagation process. To address this issue, this study first constructs a social network influence maximization propagation model, and then combines auto-encoder and graph convolutional autoencoder to extract social network user attributes. Finally, Transformer is used to learn the feature representation of social network nodes. Moreover, a heterogeneous graph neural network is introduced to combine with Transformer to further learn the feature representation of nodes, so as to design an influence maximization algorithm combining attributed graph clustering and heterogeneous graph Transformer. The results showed that the loss values of the fusion algorithm on different datasets were 0.619 and 0.17, respectively, proving its good fitting performance. The recall rate and F1 of this fusion algorithm were 92.5 % and 0.90, respectively, proving its high clustering accuracy. The influence maximization model based on fusion algorithm achieved active node coverage of 67 % and 48 % on two datasets, respectively, proving its good influence propagation effect. The above results demonstrate that the designed model can effectively spread influence. This helps to better understand and utilize the influence dissemination mechanisms in social networks, thereby promoting the development of social network research.