Network Users Research Articles

Internet fraud as one of the types of fraud

The article investigates the current aspects of internet fraud in the context of the coronavirus pandemic and the Russian-Ukrainian war. The increased use of the Internet during quarantine and wartime creates new opportunities for scammers, who exploit this situation to deceive people. Emotional instability, financial problems, and the spread of misinformation also contribute to the growth of fraud online. The article analyzes the legislative approach to combating fraud in Ukraine and identifies the specifics of this type of criminal offense. Due to the accessibility of the Internet, scammers quickly adapt their schemes to the digital environment, making network users more vulnerable. The explores the diversity of methods and types of internet fraud, including phishing, cryptocurrency fraud, social engineering, and other schemes. The authors provide data from the «Data Breach and Incident Response» (DBIR) report. Methods such as phishing, farming, fraud through social media and email, as well as manipulations with ICOs and crypto investment funds, are examined in detail. Special attention is paid to fraud in wartime, mentioning fundraising for military purposes, deceptive schemes with family announcements, and fake evacuation messages. The level of vulnerability of users, geographical features, and the age category most often targeted by internet fraud are evaluated. The article also discusses the aspect of computer equipment, mobile devices, and other means of accessing the Internet used by criminals to carry out fraudulent activities. The authors emphasize the prevalence of internet fraud, where perpetrators attempt to hack websites of government organizations, banks, or media to obtain confidential information or spread fake news. Measures to combat this phenomenon are proposed. Various types of internet fraud and methods of prevention are examined in detail, including software updates, installation of antivirus software, use of strong passwords, and two- factor authentication. It is noted that awareness and a responsible approach to protection on the Internet are key to preventing fraud. Additionally, the criminal liability for fraud in Ukraine and the possibility of strengthening this liability in wartime are discussed.

Wireless powered NOMA-based cognitive radio for 6G networks

The need for physically charging mobile devices is anticipated to become a thing of the past in the not-too-distant future. In this paper, a novel approach for wireless-powered mobile devices is presented, which is based on non-orthogonal multiple access (NOMA) and multi-user multiple-input multiple-output (MU-MIMO) antenna systems. The proposed method is specifically designed for use in a cognitive underlay radio (CR) scenario, where the primary network requires a certain quality of service (QoS). Meanwhile, secondary network users can download their data using the same spectrum. The main objective of this paper is to maximize the sum rate of the secondary network. We have optimized a joint beamforming vector for both primary and secondary networks and a time-switching coefficient for energy harvesting and information transfer to achieve this objective. The initial problem formulation was non-convex, requiring the implementation of various techniques to ensure an efficient and low-complexity algorithm. To this end, we employed semi-definite programming, successive convex approximation, and alternating optimization. The proposed NOMA-based solution outperforms the TDMA-based benchmark scheme regarding sum data rate, specifically in the low transmit power region.

Decomposing total power flows into contributions originating from power sources in loads, generators, and network elements

To thoroughly analyze the conditions in power lines, transformers or loads, it is essential to have knowledge of their active and reactive power flow contributions from each power source. Understanding these contributions allows for a detailed analysis of the impact of active and reactive power flow contribution from each power source on active and reactive power losses in each network element, as well as the determination of power participation of each source to every load. This paper provides the necessary equations and an adequate procedure for calculating power flow contributions. The method is suitable for meshed network and considers shunt elements, avoiding problematic simplifications. Furthermore, it ensures impartiality towards any network user, making it well-suited for application in deregulated environments. By offering valuable information useful, this method supports the creation of transparent and non-discriminatory agreements between network operator and users.

Adaptive QoE‐based video delivery by optimizing the bitrate of the video course using network bandwidth and user bandwidth

SummaryWith the increase in internet connectivity worldwide, people have resorted to various video streaming applications for entertainment, education, healthcare, and so on. However, this has resulted in huge internet traffic, which is predominantly occurring due to the increased transmission of videos over wireless networks to mobile devices, Transmission of ultra‐high‐definition videos can be effortlessly carried out, although the video quality perceived by the users is normally lesser than the anticipated quality. In video streaming applications, it is a pre‐requisite to provide users with high quality of experience (QoE) but this is generally affected by the dynamic variations in the network conditions. This work presents a novel QoE‐based video delivery system in a collaborative e‐learning platform. Here, a novel deep learning structure recurrent neural network‐long short term memory (RNN‐LSTM) is developed for adaptive bit rate (ABR) selection, thereby providing users with videos of high QoE. Various network, time, and buffer‐based parameters are considered while selecting the bit rate. Additionally, the proposed RNN‐LSTM is assessed for its superiority in providing high QoE videos based on measures, like QoE, buffer size, and bit rate, and is observed to attain values of 0.975, 45.654, and 90.766 b/s, respectively.

Conditional heavy hitter monitoring and application of heterogeneous graph streams based on sketches

Graph streams offer valuable insights into real network evolution in the era of big data. Heavy hitters and conditional heavy hitters focus on identifying entities with significant network traffic, revealing network data characteristics. In this paper, we present two methods, BLSketch and MHLSketch, for identifying heavy hitters and conditional heavy hitters in heterogeneous graph streams. BLSketch uses the bidirectional mapper, Bimap, for efficient query response times while retaining candidate information. MHLSketch uses min heap to preserve a minimal set of candidates to minimize storage space, excelling in space efficiency. The two methods can be incorporated with any sketch based graph summarization method, showing excellent adaptability. Experimental results on four real-world heterogeneous datasets of varying scales with the largest dataset containing millions of edges demonstrate our methods significantly improve the quality of query answering, outperforming state-of-the-art methods, with precision close to 1 and average relative error close to 0. We further combine our methods with density clustering to introduce the SDC model, which efficiently identifies hot regions and key users in Location-Based Social Networks (LBSN). In experiments, SDC outperforms traditional clustering by reducing up to 93.1% of cold Points of Interest (POIs) and shows strong potential for practical applications.

The Data Crowd as a Legal Stakeholder

Abstract This article identifies a new legal stakeholder in the data economy: the data crowd. A data crowd is a collective that: (i) is unorganised, non-deliberate and unable to form an agenda; (ii) relies on productive aggregation that creates an interdependency among participants; and (iii) is subjected to an external authority. Notable examples of crowds include users of a social network, users of a search engine and users of artificial intelligence-based applications. The law currently only protects users in the data economy as individuals, and in certain cases may address broad public concerns. However, it does not recognise the collective interests of the crowd of users and its unique vulnerability to platform power. The article presents and defends the crowd’s legal interests in a stable infrastructure for participation. It therefore reveals the need for a new approach to consumers’ rights in the data economy.

Demystifying the Effect of Social Media Advertising on Purchase Intention of Millennials: Role of eWOM and Privacy Concerns

The substantial impact of businesses’ strong online presence on consumers’ spending habits, especially among decision-makers, cannot be overstated. One of the forms of online product reviews is electronic word-of-mouth (eWOM), and it has a significant impact on consumers’ attitude towards a product as well as their intentions to purchase. This article aims to provide a conceptual framework that assesses how eWOM and privacy issues affect Indian millennials’ attitude towards social media advertising and the subsequent effect of attitude on purchase intention. The data were gathered through non-probability sampling from 340 social network users utilising an online questionnaire through Google Forms. The adapted scales were validated using confirmatory factor analysis, followed by path analysis using SPSS AMOS 26.0 to examine numerous hypotheses that had been formulated. The results revealed significant relationships that are helpful in recognising the attitude and purchase intention of Indian millennials regarding social media advertisements. The study can be helpful to brand managers, marketing executives, and advertisers in generating social media commercials by incorporating key elements that can positively mould attitudes and further develop purchase intention.

An Amalgamated Kruskal’s Algorithm to ascertain the shortest route in the futuristic smart world

As the globe enters a new era, web applications will become indispensable to managing business. Businesses can easily grow, become simpler, and accomplish their objective much faster by employing web applications. Creating a web application in cloud computing allows for the more affordable leveraging of cloud-based services. This makes it easier to avoid setting up and maintaining several servers. To get around cloud computing’s built-in restrictions such as scalability, security, and bandwidth limitations, the future smart world of cloud computing will be coupled with LiFi connectivity. Beyond creating the web application, it is important to promote this web application among the network of users as quickly and effectively as possible. This manuscript proposes a strategy to address these challenges. There are two primary components to this MCDM technique. The first step is to model the problem as a graph and weigh the edges by employing the Hamacher aggregation operator. The second step involves using a fresh iteration of Kruskal’s technique in conjunction with this approach to discover a Minimum Spanning Tree as a resolution. This manuscript adds to the literature by solving real-world Minimum Spanning Tree problems by combining existing algorithms with MCDM techniques. This technique is demonstrated for marketing a web application(created via cloud service) in a future smart world using LiFi technology.

A Review of Machine Learning Approaches for Rumour Detection: Techniques, Challenges, and Future Directions (Machine Learning for Rumour Detection)

Rumours and misinformation propagate rapidly across online social networks, posing significant challenges to maintaining the integrity of information dissemination. In recent years, machine learning (ML) techniques have emerged as promising tools for automating the detection and mitigation of rumours. This review paper provides a comprehensive examination of the advancements in rumour detection using ML approaches. The paper begins by outlining the landscape of rumour dissemination in online social networks, highlighting the characteristics and challenges associated with rumour detection. Subsequently, it systematically categorizes and analyzes various ML methods employed for rumour detection, including supervised, unsupervised, and semi-supervised learning approaches. Furthermore, the review delves into the diverse features and representations utilized in ML models for rumour detection, such as textual content, user engagement patterns, network structures, and temporal dynamics. It discusses the strengths and limitations of different feature sets and their impact on the effectiveness of rumour detection systems. Moreover, the paper explores the intricacies of dataset construction and evaluation methodologies for training and testing rumour detection models. It examines commonly used benchmark datasets and evaluation metrics, emphasizing the importance of robust evaluation frameworks for assessing the performance of ML-based rumour detection systems accurately. Additionally, the review identifies key challenges and open research questions in the field of rumour detection using ML, including handling evolving rumour patterns, addressing adversarial attacks, and enhancing the interpretability and explain ability of ML models. It also discusses potential directions for future research aimed at advancing the state-of-the-art in rumour detection and mitigation.

СПРОЩЕНА ІМІТАЦІЙНА МОДЕЛЬ НАВАНТАЖЕННЯ В ЗАМКНЕНІЙ МЕРЕЖІ КОМУТАЦІЇ ПАКЕТІВ

An approach to simplified simulation modeling of packet switching network load has been developed. The simulated representation of network processes is based on a simplified mathematical model of the packet switching network, which is based on a dis-cretized description of the load states of the network nodes and corresponds to the repre-sentation of the current state of the network in the tasks of managing its load. Changes in the node's filling state are modeled as a process of reproduction and death, and in the full load model, two factors of change are taken into account: the node's exchange of load with network users and transit load flow passing through it. To study an artificially se-lected "part of the network functioning process" - an approach to transit load modeling in a load-locked network is proposed. The approach allows for significant simplifications and ensures the leveling of the negative impact of simplifications on "load closure" (en-sures the rule of constant amount of load in a closed network). The developed simulation model, firstly, creates the basis for the improvement of the complete model, and secondly, it is convenient for working out routing algorithms when analyzing them from the point of view of indicators of the ability to "influence" the load in the network. The obtained re-sults are generalized, but attention is focused on the example of satellite packet switching networks.

Resource allocation of cognitive relay networks under continuous transmission mechanism

This paper considers the joint power allocation, path selection and subcarrier pairing design of cognitive radio cooperative networks, where the transmission power of secondary users is limited by its own power budget and the tolerable interference threshold of primary users. In particular, a continuous transmission mechanism rather than intermittent emission, leading to severe signal interference between primary and secondary users, as well as mutual interference between secondary users, is taken into account. This cooperative cognitive radio network is seen as an improvement in the sense that the source transmitter can continuously transmit symbols without silence. Although traditional cooperative network technology has long been applied to the cognitive radio field and it has been proposed to allow continuous transmission signals by source transmitter, the deployment of such improved cooperative behavior in cognitive radio networks has not been reported yet. This improvement assumption may render serious mutual interference between the relay station and the destination receiver, and the signal interference borne by the primary user will also exceed the threshold value, if there is no elaborate signal design and resource scheduling. In order to make full use of the available resources, this paper presents the closed-form solution of the power allocation, the decision criteria of the path selection and the subcarrier pairing technique for the problem of maximization of the sum rate of secondary users in such networks under the dual constraints of the power budget and the interference threshold. Simulation results show that the continuous transmission behavior at the source significantly outperforms the traditional switching back and forth between on and off.

Long-term Monitoring of Didymos with the LCOGT Network and MRO after the DART Impact

The world’s first planetary defense test mission was carried out in late 2022 by NASA’s Double Asteroid Redirection Test (DART) mission. The main DART spacecraft, which was accompanied by the ASI-provided LICIACube cubesat, intentionally impacted Dimorphos, the smaller secondary of the near-Earth object binary system (65803) Didymos, on 2022 September 26. The impact released a large amount of ejecta, which, combined with the spacecraft’s momentum, produced the observed 33 ± 1 minute period change that was subsequently observed from ground-based telescopes. The DART mission, in addition to having successfully changed the orbital period of Dimorphos, also activated the asteroid as a result of the impact but under known conditions, unlike other impacts on asteroids. We have conducted long-term monitoring over 5 months following the impact with the Las Cumbres Observatory Global Telescope (LCOGT) network and Magdalena Ridge Observatory (MRO). This was supplemented by almost 3 months of more sparsely sampled data, primarily from educational users of the LCOGT network during the period from 2022 July 5 to 2022 September 25, prior to the impact date of 2022 September 26. Here we report the observations of the Didymos system and DART impact ejecta with the telescopes of the LCOGT network from T+1.93 days to T+151.3 days after impact, and we study the evolving morphology of the ejecta cloud and evolving tail over the entire length of the data set. In addition, we combined these intensive data sets with the earlier sparse observations over the ∼90 days prior to impact to derive a new disk-integrated phase function model using the H, G 1, G 2 parameterization.

Resource Allocation and Outage Probability Optimization Method for Multi-Hop UAV Relay Network for Servicing Heterogeneous Users

Resource Allocation and Outage Probability Optimization Method for Multi-Hop UAV Relay Network for Servicing Heterogeneous Users

AQUA: Analytics-driven quantum neural network (QNN) user assistance for software validation

This paper proposes a novel analytics-driven user assistance software validation approach for quantum neural network (QNN) codes. The proposed analytics-driven QNN user assistance (AQUA) for software validation considers user interactive feedback for constructing efficient QNN software. Our proposed AQUA is based on dynamic software testing and analysis due to undetermined qubit states in QNN which is hard to be tracked via static software analysis. AQUA is for plotting gradient variances to determine whether the QNN software suffers from local minima situations, which are called barren plateaus in QNN. By utilizing AQUA software validation, the stability, feasibility, and explainability of QNN software can be tested. AQUA has been tested using real-world case study with quantum convolutional neural network software for point cloud data processing in autonomous driving applications.

Unraveling the root causes of faults in mobile communications: A comparative analysis of different model explainability techniques

The escalating demand and complexity of monitoring services handled by Network Operations Centers (NOCs) have led Mobile Network Operators (MNOs) to prioritize automated solutions for network fault detection and diagnosis. Consequently, various Machine Learning (ML)-based Root Cause Analysis (RCA) systems have been developed, however their lack of explainability poses a challenge due to the predominantly black-box nature of ML models. This paper addresses this issue by presenting a supervised clustering methodology capable of integrating both glass-box and black-box models, the latter complemented by post-hoc explainability techniques. While black-box models excel in predictive capabilities, necessitating post-hoc techniques for explainability, glass-box models prioritize transparent decision-making, fostering a clearer understanding of the model’s behavior. This work delineates a methodology for performing RCA of faults in the User Downlink (DL) Average Throughput Key Performance Indicator (KPI), simultaneously comparing the performance of black-box models (Light Gradient-Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost)) with glass-box models (Logistic Regression (LR) and Explainable Boosting Machine (EBM)). Results revealed that the LightGBM black-box algorithm coupled with the SHapley Additive exPlanations (SHAP) method demonstrated superior performance in fault detection and diagnosis, without compromising the overall explainability. Consequently, it was possible to identify faults related to radio conditions, low network usage in specific user groups, low network capacity, and mobility issues. The paper concludes with practical mitigation strategies for each identified fault cluster.

DEVELOPMENT OF ACADEMIC ADVISING INFORMATION SYSTEM APPLICATIONS AT MAKASSAR STATE UNIVERSITY USING THE ANDROID PLATFORM

Advising is the process where students communicate about issues that may arise during their study period. However, it is often hindered by the busy schedules and different timetables between students and their academic advisors. Therefore, a system has been developed to facilitate the advising process using the Android platform. This research adopts the Research and Development (R&D) methodology, specifically using the waterfall development model. Data collection techniques include interviews and questionnaires. The application is tested based on the ISO/IEC 25010 quality standards, covering four aspects: functional suitability, performance efficiency, usability, and portability. The functional suitability testing involved two expert professors, and 24 features were tested, indicating that the application is "Suitable" for use. Portability testing on the application was conducted by observing the output on three Android versions: Nougat, Red Velvet Cake, and Tiramisu. The results showed that the application can run on all three Android versions. Performance efficiency testing utilized the Android Profiler tool to assess CPU, memory, and network usage while running the application. The findings revealed that CPU usage was less than 50%, memory allocation was 122.2 MB, and network usage was less than 48 KB/s. Usability testing conducted by students resulted in a test score of 81.6%, categorized as "Very Good" feasibility.

You change the way you talk: Examining the network, toxicity and discourse of cross-platform users on Twitter and Parler during the 2020 US Presidential Election

This study examines code-switching behaviours of cross-platform social media users specifically between Twitter and Parler during the 2020 US Presidential Election. Utilising social identity theory as a framework, we examine messages related to voter fraud by users who migrated from Twitter to Parler following Twitter bans. Our analysis covers 38,798 accounts active on both platforms, analysing 1.5 million tweets and more than 100,000 parleys. The key findings of the study are as follows: First, we discovered differing levels of network homophily between high degree centrality and low-degree centrality cross-platform users, illustrating how individuals with varying degrees of influence engage differently across platforms. Second, we observed higher toxicity levels in heterogeneous networks, which include both in-group and out-group members, compared with homogeneous networks that are primarily composed of in-group members. This suggests the level of toxicity in online spaces correlates with the level of group diversity. Third, we found that cross-platform users created distinctive discourse community with in-group and out-group members, indicating that content and discussions within these networks are influenced by the social identity dynamics of the users. Our study contributes to the current research in political communication and information science by proposing comparative user analyses across multiple social media platforms. Focusing on a critical period of platform transition during a contentious political event, our study offers insights into the dynamics of online communities and the shifting nature of political language used by social media users.

Workload prediction based on improved error correlation logistic regression algorithm and Cross‐TRCN of spatiotemporal neural network

AbstractIn view of the randomness of user network usage behavior in data centers, which leads to a large randomness in power load, and considering that a single randomness processing method is usually difficult to fully characterize the uncertain characteristics of the system, this paper proposes a dual fusion prediction analysis model based on an improved error correlation logic regression algorithm and a novel spatiotemporal neural network structure called Cross‐TRCN. Two weight coefficients λ1 and λ2 are introduced to fuse the prediction results with different long‐term sequence prediction performance, thereby further eliminating the influence of random errors. The results show that it is feasible to predict the workload of data centers based on the improved error correlation logic regression algorithm and the innovative spatiotemporal neural network structure Cross‐TRCN.

Joint Base Station Selection and Power Allocation Design for Reconfigurable Intelligent Surface-Aided Cell-Free Networks

Cell-free (CF) networks can reduce cell boundaries by densely deploying base stations (BSs) with additional hardware costs and power sources. Integrating a reconfigurable intelligent surface (RIS) into CF networks can cost-effectively increase the capacity and coverage of future wireless systems. This paper considers an RIS-aided CF system where each user is supported by a devoted RIS and can establish connections with multiple BSs for coherent transmission. Specifically, each RIS can enhance signal transmission between users and their selected BSs through passive beam-forming, but also randomly scattered signals from other non-selected BSs to users, causing additional signals and interference in the network. To gain insights into the system performance, we first derive the average signal-to-interference-plus-noise ratio (SINR) received by each user in a closed-form expression. Subsequently, we formulate an optimization problem aimed at maximizing the weighted sum-SINR of all users in the RIS-CF network. This optimization considers both BS transmit power allocation and BS selections as variables to be jointly optimized. To tackle the complexity of this nonconvex optimization problem, we develop an innovative two-layer iterative approach that offers both efficiency and efficacy. This algorithm iteratively updates the transmit power allocation and BS selections to converge to a locally optimal solution. Numerical results demonstrate significant performance improvement for the RIS-CF network using our proposed scheme. These results also highlight the effectiveness of jointly optimizing BS transmit power allocation and BS selections in the RIS-CF network.

SIMPLIFIED SIMULATION MODEL OF LOAD IN A CLOSED PACKET SWITCHING NETWORK

An approach to simplified simulation modeling of packet switching network load has been developed. The simulated representation of network processes is based on a simplified mathematical model of the packet switching network, which is based on a discretized description of the load states of the network nodes and corresponds to the representation of the current state of the network in the tasks of managing its load. Changes in the node's filling state are modeled as a process of reproduction and death, and in the full load model, two factors of change are taken into account: the node's exchange of load with network users and transit load flow passing through it. To study an artificially selected "part of the network functioning process" - an approach to transit load modeling in a load-locked network is proposed. The approach allows for significant simplifications and ensures the leveling of the negative impact of simplifications on "load closure" (ensures the rule of constant amount of load in a closed network). The developed simulation model, firstly, creates the basis for the improvement of the complete model, and secondly, it is convenient for working out routing algorithms when analyzing them from the point of view of indicators of the ability to "influence" the load in the network. The obtained results are generalized, but attention is focused on the example of satellite packet switching networks.