Network Entities Research Articles

Can social networks improve job search behaviours among low-income youth in resource-limited settings? Evidence from South Africa

ABSTRACT Previous studies have established the importance of social networks in determining youth employment outcomes. The quality and quantity of social entities in social networks and effectively using them, have a positive influence on employment outcomes. However, limited evidence exists on the composition and role of social networks on youth employment in resource-limited countries. Our study addresses current evidence gaps by investigating the association of social networks and job search behaviours in a sample of South African youth who are neither in employment, education, or training (NEET). Our results indicate that the association of social networks with job search behaviours depends on the type of social network and job search behaviours. Having more people in youth’s social network was associated with a higher likelihood of attending a job interview but no association with job applications’ submission. Additional family members were positively associated with job interviews, as well. Age, gender, relationship status, geographic residence, formal postsecondary education, training experience, caregiver status, and mobile phone ownership were also associated with job search behaviours. Overall, our findings indicate that social networks, particularly family members, are more predictive of job interviews than job applications.

Security of Zero Trust Networks in Cloud Computing: A Comparative Review

Recently, networks have shifted from traditional in-house servers to third-party-managed cloud platforms due to its cost-effectiveness and increased accessibility toward its management. However, the network remains reactive, with less accountability and oversight of its overall security. Several emerging technologies have restructured our approach to the security of cloud networks; one such approach is the zero-trust network architecture (ZTNA), where no entity is implicitly trusted in the network, regardless of its origin or scope of access. The network rewards trusted behaviour and proactively predicts threats based on its users’ behaviour. The zero-trust network architecture is still at a nascent stage, and there are many frameworks and models to follow. The primary focus of this survey is to compare the novel requirement-specific features used by state-of-the-art research models for zero-trust cloud networks. In this manner, the features are categorized across nine parameters into three main types: zero-trust-based cloud network models, frameworks and proofs-of-concept. ZTNA, when wholly realized, enables network administrators to tackle critical issues such as how to inhibit internal and external cyber threats, enhance the visibility of the network, automate the calculation of trust for network entities and orchestrate security for users. The paper further focuses on domain-specific issues plaguing modern cloud computing networks, which leverage choosing and implementing features necessary for future networks and incorporate intelligent security orchestration, automation and response. The paper also discusses challenges associated with cloud platforms and requirements for migrating to zero-trust architecture. Finally, possible future research directions are discussed, wherein new technologies can be incorporated into the ZTA to build robust trust-based enterprise networks deployed in the cloud.

A novel multidimensional trust evaluation and fusion mechanism in fog-based Internet of Things

With the fast advancement of IoT, it has gained more attention from industry and academia. However, because diverse security attacks result in the lack of trust between IoT edge devices and the network, a proper trust mechanism is necessary. To address the issue, a novel trust management approach is proposed in this work. In particular, we have proposed a multidimensional trust evaluation mechanism to comprehensively evaluate the trustworthiness of IoT edge devices, with diverse opinions formed by different network entities after observing. Next, a novel trust fusion scheme named JSDM-AHP that integrates the objective Jensen–Shannon divergence measure (JSDM) and analytic hierarchy process (AHP) is developed to fuse diverse decision factors. Theoretical analysis and simulation results show that the proposed trust strategy has high computation-efficiency, scalability, resilience, robustness, and outperforms all the other compered methods in reliability. Furthermore, with the proposed security approach, diverse cyber attacks such as flooding attacks, random attacks, conflicting behavior attacks, on–off attacks, and recommendation attacks can well be defended against, reflecting the high security of the method.

Detecting ShadowsocksR User Based on Intelligence of Cyber Entities

ShadowsocksR (SSR), as a typical emerging anonymous communication tool, may record user information on the SSR client or server, leading to the theft of the user’s privacy, and may be used by attackers to anonymize their internal network environment and organization, which will cause serious damage to data security and bring severe challenges to security defense and threat assessment within organizations. To solve the problem of accurately and effectively discovering SSR users within an organization in a real traffic environment, in this paper, we propose an SSR user detection method based on network entity intelligence as follows: (1) According to the communication characteristics of SSR users, relevant network entity intelligence information from inside and outside the organization is obtained, such as the distribution of IP addresses within and outside the organization, and the differences between SSR and non-SSR users are analyzed to construct a feature space. (2) The communication behaviors of SSR and non-SSR users are further analyzed and features are extracted from the perspective of traffic behavior analysis, and the feature space of the SSR user detection model is expanded. (3) A data-driven machine-learning-based approach is designed and implemented to provide suggestions for the automatic identification of SSR users based on the extracted feature vectors. Results show that the detection method proposed in this paper has a detection accuracy of more than 95% for SSR users in the experimental environment, can accurately distinguish between SSR communication and normal communication, and can achieve accurate SSR user detection.

Design and Implementation of Enhanced Programmable Data Plane Supporting ICN Mobility

Information-centric networking (ICN) separates the identifier and locator of network entities, providing a natural advantage in supporting mobility. To gain the advantage of ICN to support mobility, an urgent challenge is the problem of practical implementation with performance optimization. Software-defined networking (SDN) can be regarded as infrastructure to implement ICN mobility. However, it is difficult for the centralized SDN controller to quickly process mobile signaling. Therefore, this paper proposes enhanced programmable data plane supporting ICN mobility. By offloading mobility-related control plane functions from the controller to the data plane, the data plane can locally process mobile signaling without interacting with the controller. We propose an offloading mechanism for control plane functions, based on a rule table, where the controller authorizes the data plane to process the mobile signaling by loading the programmable rule table to data plane’s control element, and the control element intercepts the mobile signaling, matches the predefined rule table, and executes a series of application logic actions. In addition, we propose an improved SmartSplit algorithm to manage the rule table and speed up packets matching the rule table. Based on Intel’s Data Plane Development Kit (DPDK), we implement the enhanced programmable data plane. Our experimental results prove that the proposed enhanced programmable data plane has a stronger ability to process mobile signaling and reduce latency.

Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them

Over the past decade, new technologies have driven the rise of what is being termed as the fourth industrial revolution. The introduction of this new revolution is amalgamating the cyber and physical worlds, bringing with it many benefits, such as the advent of industry 4.0, the internet of things, cloud technologies and smart homes and cities. These new and exciting areas are poised to have significant advantages for society; they can increase the efficiency of many systems and increase the quality of life of people. However, these emerging technologies can potentially have downsides, if used incorrectly or maliciously by bad entities. The rise of the widespread use of sensor networks to allow the mentioned systems to function has brought with it many security vulnerabilities that conventional “hard security” measures cannot mitigate. It is for this reason that a new “soft security” approach is being taken in conjunction with the conventional security means. Trust models offer an efficient way of mitigating the threats posed by malicious entities in networks that conventional security methods may not be able to combat. This paper discusses the general structure of a trust model, the environments they are used in and the attack types they are used to defend against. The work aims to provide a comprehensive review of the wide assortment of trust parameters and methods used in trust models. The work discusses which environments and network types each of these parameters and calculation methods would be suited to. Finally, a design study is provided to demonstrate how a trust model design will differ between two different industry 4.0 networks.

Detecting Unknown Threat Based on Continuous-Time Dynamic Heterogeneous Graph Network

Unknown threats have caused severe damage in critical infrastructures. To solve this issue, the graph-based methods have been proposed because of their ability for learning complex interaction patterns of network entities with discrete graph snapshots. However, such methods are challenged by the computer networking model characterized by the natural continuous-time dynamic heterogeneous graph (CDHG). In this paper, we propose a CDHG-based graph neural network model, namely, CDHGN, for unknown threat detection. It first constructs the CDHG using interaction relationships among network entities extracted from various log records. Then, it trains the detection model based on a heterogeneous attention network and performs streaming detection for live online network events. We implement a prototype and conduct extensive experiments on a comprehensive cybersecurity dataset with more than nine million records. Experimental result shows that the proposed method can achieve superior detection performance than the state-of-the-art methods.

Water transport in epoxy/polyurethane interpenetrating networks

AbstractDiffusion of water in epoxy–polyurethane (PU) interpenetrating networks was investigated using gravimetry, time‐resolved ATR‐FTIR, and 2D correlation analysis. In this study, the amount of PU component and the length of polyethylene oxide soft block in them were systematically varied to determine their influence on water uptake. The diffusion coefficients were calculated and anomalous water diffusion is well described by the Carter and Kibler model. Water molecules of four different states were identified in the network volume, and sequence of their formation was determined by 2D‐IR spectroscopy. One of them could be confined into free volume (microvoids) or molecularly dispersed with hydrogen‐bonding at cluster formation (bulk dissolved), while the others could be attributed to the bound water forming strong hydrogen bonds with hydrophilic entities of networks. These results were supported by solid‐state 2H NMR data. The influence of water uptake on thermomechanical behavior of epoxy/PU networks was determined. It was shown that the increase of PU content in composites leads to the changes of the effect of water uptake from plasticizing to the anti‐plasticizing as a result of the formation of new networks of interchain hydrogen bonds.

Leveraging Dynamic Heterogeneous Networks to Study Transnational Issue Publics. The Case of the European COVID-19 Discourse on Twitter.

The ongoing COVID-19 pandemic constitutes a critical phase for the transnationalization of public spheres. Against this backdrop, we ask how transnational COVID-19 related online discourse has been throughout the EU over the first year of the pandemic. Which events triggered higher transnational coherence or national structuration of this specific issue public on Twitter? In order to study these questions, we rely on Twitter data obtained from the TBCOV database, i.e., a dataset for multilingual, geolocated COVID-19 related Twitter communication. We selected corpora for the 27 member states of the EU plus the United Kingdom. We defined three research periods representing different phases of the pandemic, namely April (1st wave), August (interim) and December 2020 (2nd wave) resulting in a set of 51,893,966 unique tweets for comparative analysis. In order to measure the level and temporal variation of transnational discursive linkages, we conducted a spatiotemporal network analysis of so-called Heterogeneous Information Networks (HINs). HINs allow for the integration of multiple, heterogeneous network entities (hashtags, retweets, @-mentions, URLs and named entities) to better represent the complex discursive structures reflected in social media communication. Therefrom, we obtained an aggregate measure of transnational linkages on a daily base by relating these linkages back to their geolocated authors. We find that the share of transnational discursive linkages increased over the course of the pandemic, indicating effects of adaptation and learning. However, stringent political measures of crisis management at the domestic level (such as lockdown decisions) caused stronger national structuration of COVID-19 related Twitter discourse.

Enabling effective location-based services for road networks using spatial mining

A co-location pattern represents a subset of Boolean spatial attributes whose instances are located in a close geographic space. These patterns are important for location-based services. There are many methods for co-location pattern mining where the distance between the events in close geographic proximity is calculated using a straight-line distance called Euclidean distance. Since most of the real-time tasks are bounded to the road networks, the results computed using Euclidean distance is not appropriate. So to compute co-location patterns involving network we define a model where initially a network model is defined and the neighbourhood is obtained by using network distance. By comparing this approach with the previous Euclidean approach, the results obtained for co-location patterns on a road network are accurate. Our experimental results for synthetic and real data show that the proposed approach is efficient and accurate for identifying co-location patterns involving network entities.

A fog-assisted system to defend against Sybils in vehicular crowdsourcing

Technological advancements in vehicular transportation systems have led to the growth of novel paradigms, in which vehicles and infrastructures collaborate to infer high-level knowledge about phenomena of interest. Vehicular Social Network (VSN) is one such paradigm in which vehicular network entities are considered as part of an Online Social Network (OSN), paving the way for new services derived from social context. Although vehicular crowdsourcing has tremendous benefits, its deployment in real systems requires to solve important challenges including defense against Sybil attacks. This paper proposes a novel fog-assisted system that uses SybilDriver to minimize the presence of Sybil entities in VSN-based crowdsourcing applications. The proposed system exploits the characteristics of Vehicular Ad-hoc NETworks (VANETs) and OSNs to effectively recognize Sybils, and the adoption of fog computing helps reduce the overall network overhead by processing data closer to the vehicles. We perform detailed experiments on real-world publicly available datasets primarily to assess the effectiveness of SybilDriver against different Sybil attack strategies. Our experimental results show that SybilDriver detects Sybils with higher performance than state-of-the-art techniques under different settings. Furthermore, an evaluation of the fog architecture in terms of message complexity demonstrates low impact on the network overhead.

Optimal Frameworks for Detecting Anomalies in Sensor-Intensive Heterogeneous Networks

Many network/graph structures are continuously monitored by various sensors that are placed at a subset of nodes and edges. The multidimensional data collected from these sensors over time create large-scale graph data in which the data points are highly dependent. Monitoring large-scale attributed networks with thousands of nodes and heterogeneous sensor data to detect anomalies and unusual events is a complex and computationally expensive process. This paper introduces a new generic approach inspired by state-space models for network anomaly detection that can utilize the information from the network topology, the node attributes (sensor data), and the anomaly propagation sets in an integrated manner to analyze the entire network all at once. This article presents how heterogeneous network sensor data can be analyzed to locate the sources of anomalies as well as the anomalous regions in a network, which can be impacted by one or multiple anomalies at any time instance. Experimental results demonstrate the superior performance of our proposed framework in detecting anomalies in attributed graphs. Summary of Contribution: With the increasing availability of large-scale network sensors and rapid advances in artificial intelligence methods, fundamentally new analytical tools are needed that can integrate data collected from sensors across the networks for decision making while taking into account the stochastic and topological dependencies between nodes, sensors, and anomalies. This paper develops a framework to intelligently and efficiently analyze complex and highly dependent data collected from disparate sensors across large-scale network/graph structures to detect anomalies and abnormal behavior in real time. Unlike general purpose (often black-box) machine learning models, this paper proposes a unique framework for network/graph structures that incorporates the complexities of networks and interdependencies between network entities and sensors. Because of the multidisciplinary nature of the paper that involves optimization, machine learning, and system monitoring and control, it can help researchers in both operations research and computer science domains to develop new network-specific computing tools and machine learning frameworks to efficiently manage large-scale network data.

A multi-agent deep reinforcement learning based energy management for behind-the-meter resources

The future communities are becoming more and more electrically connected via increased penetrations of behind-the-meter (BTM) resources, specifically, electric vehicles (EVs), smart buildings (SBs), and distributed renewables. The electricity infrastructure is thus seeing increased challenges in its reliable, secure, and economic operation and control with increased and hard to predict demands (due to EV charging and demand management of SBs), fluctuating generation from renewables, as well as their plug-N-play dynamics. Reinforcement learning has been extensively used to enable network entities to obtain optimal policies. The recent development of deep learning has enabled deep reinforcement learning (DRL) to drive optimal policies for sophisticated and capable agents, which can outperform conventional rule-based operation policies in applications such as games, natural language processing, and biology. Furthermore, DRL has shown promising results in many resource management tasks. Numerous studies have been conducted on the application of single-agent DRL to energy management. In this paper, a fully distributed energy management framework based on multi-agent deep reinforcement learning (MADRL) is proposed to optimize the BTM resource operations and improve essential service delivery to community residents.

Performance Analysis of Selected Machine Learning Techniques for Estimating Resource Requirements of Virtual Network Functions (VNFs) in Software Defined Networks

Rapid development in the field of computer networking is now demanding the application of Machine Learning (ML) techniques in the traditional settings to improve the efficiency and bring automation to these networks. The application of ML to existing networks brings a lot of challenges and use-cases. In this context, we investigate different ML techniques to estimate resource requirements of complex network entities such as Virtual Network Functions (VNFs) deployed in Software Defined Networks (SDN) environment. In particular, we focus on the resource requirements of the VNFs in terms of Central Processing Unit (CPU) consumption, when input traffic represented by features is processed by them. We propose supervised ML models, Multiple Linear Regression (MLR) and Support Vector Regression (SVR), which are compared and analyzed against state of the art and use Fitting Neural Networks (FNN), to answer the resource requirement problem for VNF. Our experiments demonstrated that the behavior of different VNFs can be learned in order to model their resource requirements. Finally, these models are compared and analyzed, in terms of the regression accuracy and Cumulative Distribution Function (CDF) of the percentage prediction error. In all the investigated cases, the ML models achieved a good prediction accuracy with the total error less than 10% for FNN, while the total error was less than 9% and 4% for MLR and SVR, respectively, which shows the effectiveness of ML in solving such problems. Furthermore, the results shows that SVR outperform MLR and FNN in almost all the considered scenarios, while MLR is marginally more accurate than FNN.

From Resource Auction to Service Auction: An Auction Paradigm Shift in Wireless Networks

In 5G and beyond, the newly emerging services, such as edge computing/intelligence services, may demand the provision of heterogeneous communications, computing, and storage (CCS) resources on and across network entities multihop apart. In such cases, traditional resource-oriented auction schemes, where buyers place bids on resources, may not be effective in providing end-to-end (E2E) quality-of-service (QoS) guarantees. To overcome these limitations, in this article, we coin the concept of E2E service auction where the auction commodities are E2E services rather than certain resource. Under this framework, buyers simply bid for services with E2E QoS requirements without having to know the inner working (which resources are behind). To guarantee E2E QoS for winning bids while ensuring essential economic properties, E2E service auction requires addressing the joint problem of network optimization and auction design with both economical and QoS constraints. To substantiate the mechanism design, we illustrate how to devise E2E service auctions for edge computing systems under various scenarios. We also identify the research opportunities on E2E service auction mechanism design for other critical use cases, including edge intelligence.

A Dynamic Cache Allocation Mechanism (DCAM) for Reliable Multicast in Information-Centric Networking

As a new network architecture, information-centric networking (ICN) decouples the identifiers and locators of network entities and makes full use of in-network cache technology to improve the content distribution efficiency. For reliable multicast, ICN in-network cache can help reduce the loss recovery delay. However, with the development of applications and services, a multicast tree node often serves multiple reliable multicast groups. How to reasonably allocate cache resources for each multicast group will greatly affect the performance of reliable multicast. In order to improve the overall loss recovery performance of reliable multicast, this paper designs a dynamic cache allocation mechanism (DCAM). DCAM considers the packet loss probability, the node depth of the multicast tree, and the multicast transmission rate of multicast group, and then allocates cache space for multicast group based on the normalized cache quota weight. We also explore the performance of three cache allocation mechanisms (DCAM, AARM, and Equal) combined with four cache strategies (LCE, CAPC, Prob, and ProbCache), respectively. Experimental results show that DCAM can adjust cache allocation results in time according to network changes, and its combinations with various cache strategies outperform other combinations. Moreover, the combination of DCAM and CAPC can achieve optimal performance in loss recovery delay, cache hit ratio, transmission completion time, and overhead.

A Multi-Agent Model-Based Evolutionary Model of Port Service Value Network and Decision Preferences

The rise of value networks in the new economic era and the emergence of a common competitive advantage pose enormous challenges to the value-added approach of enterprises based on traditional value chains. Based on the customer-centered concept, the complementary effect of agents, and other competition characteristics of the value network, this paper constructs a multi-agent-based value network evolution model. The influencing mechanism of customer preference on the number of value network entities and overall income is discussed through simulation analysis. The research shows that the influence of customer preference on the evolution and steady-state of the overall value of the value network is not significant. Customer preference plays a decisive role in the number of service subjects and the evolution of attributes. In different situations, the influence of customer preference on the number of subjects is significantly different. This means that customer power preference in the value network of port services is much higher than other preferences. The research results provide a decision-making basis for enterprises to break through the shackles of traditional value-added concepts, actively seek value network optimization schemes, and achieve timely responses to customer preferences.

Fast computation of matrix function-based centrality measures for layer-coupled multiplex networks.

Centrality measures identify and rank the most influential entities of complex networks. In this paper, we generalize matrix function-based centrality measures, which have been studied extensively for single-layer and temporal networks in recent years to layer-coupled multiplex networks. The layers of these networks can reflect different relationships and interactions between entities or changing interactions over time. We use the supra-adjacency matrix as network representation, which has already been used to generalize eigenvector centrality to temporal and multiplex networks. With a suitable choice of edge weights, the definition of single-layer matrix function-based centrality measures in terms of walks on networks carries over naturally to the multilayer case. In contrast to other walk-based centralities, matrix function-based centralities are parameterized measures, which have been shown to interpolate between (local) degree and (global) eigenvector centrality in the single-layer case. As the explicit evaluation of the involved matrix function expressions becomes infeasible for medium to large-scale networks, we present highly efficient approximation techniques from numerical linear algebra, which rely on Krylov subspace methods, Gauss quadrature, and stochastic trace estimation. We present extensive numerical studies on synthetic and real-world multiplex transportation, communication, and collaboration networks. The comparison with established multilayer centrality measures shows that our framework produces meaningful rankings of nodes, layers, and node-layer pairs. Furthermore, our experiments corroborate the linear computational complexity of the employed numerical methods in terms of the network size that is theoretically indicated under the assumption of sparsity in the supra-adjacency matrix. This excellent scalability allows the efficient treatment of large-scale networks with the number of node-layer pairs of order 10^{7} or higher.

A Privacy Protection Scheme for Facial Recognition and Resolution Based on Edge Computing

Facial recognition and resolution technology have extensive application scenarios in the era of big data. It ensures the consistency of personal identity in physical space and cyberspace by establishing correspondence between physical objects and network entities. However, massive data brings huge processing pressure to cloud service, and there are data leakage risks about personal information. To address this problem, we propose a privacy security protection scheme for facial recognition and resolution based on edge computing. Firstly, a facial recognition and resolution framework based on edge computing is established, which improves the communication and storage efficiency through task partition and relieves the pressure of cloud computing. Then, a verifiable deletion scheme based on Hidden CP-ABE is proposed to provide fine-grained access control and ensure the safe deletion of target data in the cloud. Moreover, after applying the verifiable deletion method, the safe deletion of the target data in the cloud can be achieved. Finally, the simulation results show the effectiveness and security of the proposed scheme.

A LoRaWAN Network Architecture with MQTT2MULTICAST

In this work, an architecture for IoT networks oriented towards environmental sustainability is presented. Due to the suitability of its characteristics in terms of coverage, power and support of a large number of devices, an enhanced LoRaWAN network has been chosen as the basis for this proposal. The architecture is completed with the virtualization of the different LoRaWAN network entities and the usage of a software-defined network for their interconnection. The publication and subscription to environmental data is carried out by using the MQTT protocol. MQTT has been optimized thanks to the use of the SDN network and the use of edge computing resources, which allows multicasting of published data. Thanks to our developed MQTT2MULTICAST protocol, latency is improved by approx. 90% and the traffic load within the SDN network is reduced by 55%. An scalability analysis shows that this solution is able to support tens of thousands of LoRaWAN gateways. The proposed architecture has been implemented using commercial equipment as a proof of concept.