Network Users Research Articles

Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency

The growing number of AI-driven applications in mobile devices has led to solutions that integrate deep learning models with the available edge-cloud resources. Due to multiple benefits such as reduction in on-device energy consumption, improved latency, improved network usage, and certain privacy improvements, split learning, where deep learning models are split away from the mobile device and computed in a distributed manner, has become an extensively explored topic. Incorporating compression-aware methods (where learning adapts to compression level of the communicated data) has made split learning even more advantageous. This method could even offer a viable alternative to traditional methods, such as federated learning techniques. In this work, we develop an adaptive compression-aware split learning method (“deprune”) to improve and train deep learning models so that they are much more network-efficient, which would make them ideal to deploy in weaker devices with the help of edge-cloud resources. This method is also extended (“prune”) to very quickly train deep learning models through a transfer learning approach, which tradesoff little accuracy for much more network-efficient inference abilities. We show that the “deprune” method can reduce network usage by 4× when compared with a split-learning approach (that does not use our method) without loss of accuracy, while also improving accuracy over compression-aware split-learning by up to 4 percent. Lastly, we show that the “prune” method can reduce the training time for certain models by up to 6× without affecting the accuracy when compared against a compression-aware split-learning approach.

Explainable Machine Learning Models for Real-Time Threat Detection in Cybersecurity

In the rapidly evolving landscape of cybersecurity, traditional machine learning models often operate as "black boxes," providing high accuracy but lacking transparency in decision-making. This lack of explainability poses challenges for trust and accountability, especially in critical areas like threat detection and incident response. Explainable machine learning models aim to address this by making the model's predictions more understandable and interpretable to users. This research integrates explainable machine learning models for real-time threat detection in cybersecurity. Data from multiple sources, including network traffic, system logs, and user behavior, undergo preprocessing such as cleaning, feature extraction, and normalization. The processed data is passed through various machine learning models, including traditional approaches like SVM and decision trees, as well as deep learning models like CNN and RNN. Explainability techniques such as LIME, SHAP, and attention mechanisms provide transparency, ensuring interpretable predictions. The explanations are delivered through a user interface that generates alerts, visualizations, and reports, facilitating effective threat assessment and incident response in decision support systems. This framework enhances model performance, trust, and reliability in complex cybersecurity scenarios.

Perceptions of nurses from Psychosocial Care Centers (CAPS) about practices performed during home visits

OBJECTIVE: Investigate perceptions about the practices performed by nurses during home visits carried out with patients treated at Psychosocial Care Centers (CAPS). METHOD: This exploratory research with a qualitative approach was carried out with six nurses at CAPS in a countryside city of Southern Brazil. Sociodemographic questionnaires and a semi-structured interview were applied, and the data were analyzed through Content Analysis. RESULTS: Results were organized into three categories: 1) Insufficient training of nursing professionals to work in CAPS; 2) Challenges faced by the nursing team during home visits; and 3) Impacts of the support network on the recovery process of patients. FINAL CONSIDERATIONS: The nurses participating in the study reported not having received adequate training during their undergraduate studies, making it necessary to seek knowledge by their own means. In addition, several challenges were noted during visits, such as patients' resistance to receiving the multidisciplinary team, incomplete or incorrect addresses, patients' absence during home visits, and the difficulty in reaching patients' families. Finally, the importance of family assistance as a support network for users with mental disorders was noted.

The Impact of Social Networking Sites on the Family Cohesion of Algerian Society: Risks and Pros

Objective: Therefore, the aim of this study is to assess the impact of SNS on family closeness in the Algerian culture while looking at the benefits in addition to the drawbacks of using the technology. Theoretical Framework: Applying Social Capital Theory, Family Systems Theory, and Uses and Gratifications Theory allows the research to explain the connection between SNS and family cohesion and give a coherent concept of how the media affects family bonds. Method: A cross-sectional survey and qualitative interviews were used as main data collection tools from the families of different SES in Algeria; social networking usage patterns were also quantitatively measured by coded questionnaires and a qualitative approach was employed to explore the family dynamics and modes of communication. Results & Discussion: This study revealed bi-directional relationship between SNS and family connectedness ; and, the following are the majority observation : Nevertheless, some families had lesser amounts of direct confrontation and roadside fights. The findings are elaborated in regard to theoretical frameworks. Research Implications: This research emphasizes on family therapy, counseling and education, and use of internet by families. It prescribes SNS usage as a means for a healthy functioning of families. Originality/Value: This work aims at giving a cultural angle to the effects of social networking sites (SNS) on family integration in Algeria while serving to substantiate the prior research on digitisation counterpoint in the area of relationship communication.

Validating the Quality Maternal and Newborn Care Framework Index: A Global Tool for Quality-of-Care Evaluations.

Quality maternity care is known to improve a range of maternal and neonatal outcomes. The Lancet Series on Midwifery's Quality Maternal and Newborn Care (QMNC) Framework is a high-level synthesis of the global evidence on quality maternity care. Initial qualitative work demonstrated the Framework's adaptability in evaluating service user and provider perceptions of the quality of maternity care. However, evaluating services at scale requires a survey instrument. This paper reports the validation of the QMNC Framework index (QMNCFi), a five-part survey for the evaluation of maternity care across diverse settings. International online English language survey of women who had given birth in the previous year in Australia, Ghana, India and the United Kingdom (UK). It was distributed through service user networks (UK and Australia) and at postnatal clinics (Ghana and India). All forms were completed online. Test-retest was conducted to assess reliability. Five hundred and forty mothers completed the survey (Australia 136; Ghana 131; India 153; UK 120). Construct validity: Cronbach's α in 12 of the survey's 13 sections ranged from 0.795 to 0.986; for the remaining section the alpha was 0.594. Reliability: 55 participants completed the QMNCFi a second time. Intraclass correlation coefficient results ranged from 0.657 to 0.939 across the 13 sections. Field researchers in Ghana and India reported that the survey was easily understood and completed. This survey has shown that, across diverse contexts, the QMNCFi is a valid, reliable, and comprehensive tool for measuring service user perceptions of the quality of care over time.

An optimized model for network intrusion detection in the network operating system environment

With the heavy reliance on computers and information technology to send and receive data across networks of various types, there has been concern about securing that data from intrusions and cyber-attacks. The expansion of network usage has led to an increase in hacker attacks, which has led to prioritizing cybersecurity precautions in detecting potential threats. Intrusion detection techniques are a critical security measure to protect networks in both personal and corporate environments that are managed by network operating systems. For this, the paper relies on designing a network intrusion detection model. Since deep neural networks (DNNs) are classic deep learning models known for their strong classification performance, making them popular in intrusion detection along with other machine learning algorithms, they have been chosen to improve intrusion classification models based on datasets for intrusion detection systems. The basic structure of this proposal is to adopt one of the optimization algorithms in extracting features from the dataset to obtain more accurate results in the classification and intrusion detection stage. The developed Corona Virus algorithm is adopted to improve the system performance by identifying optimal features. This algorithm, which consists of several stages, optimally selects individuals based on features from the NSL-KDD dataset used for intrusion detection. The resulting optimization solution acts as a network structure for the intrusion classification model based on machine learning and deep learning algorithms. The test results showed exceptional performance on the NSL-KDD dataset, where the proposed Convolution Neural Network CNN model achieved 99.3% accuracy for multi-class classification, while the Decision Tree (DT) achieved 88.64% accuracy for anomaly detection in bi-class classification.

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).