Connect, Collaborate, And Innovate: IJCNC - Where Networking Futures Take Flight!

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The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.

Education

* Connect, Collaborate, And Innovate: IJCNC - Where Networking Futures Take Flight! *
Explore the Frontier of Networking Research with IJCNC! 🌐
Unlock the Gateway to Cutting-Edge Networking Insights!
🔍 Seeking the latest breakthroughs in Computer Networks & Communications? Look no further
than the International Journal of Computer Networks & Communications (IJCNC)!
📝 Submit your research today and join the ranks of esteemed authors contributing to the
advancement of the field!
🌟 Why Choose IJCNC?
 Rigorous peer-review process ensuring top-notch quality
 Bi-monthly open-access publication
 Embracing diverse perspectives from both academia and industry
 Focused on both technical and practical aspects of Networking & Data Communications
📈 Elevate Your Research Impact!
 Join a global community of scholars and practitioners
 Enjoy high visibility and wide readership
 Benefit from our exceptionally low acceptance rate (less than 5%)
 All papers rigorously screened for plagiarism using manual and Docoloc methods
🏆 Recognized Excellence:
 Articles published in IJCNC receive substantial citations
 Regarded as one of the premier journals in networking research
🚀 Propel Your Networking Innovations Forward! Submit your manuscript to IJCNC today and
be part of the next wave of networking advancements! Don't miss this unparalleled opportunity to
shape the future of Computer Networks & Communications!
Learn more and submit your work at - https://airccse.org/journal/ijcnc.html
"Connect, Collaborate, And Innovate: IJCNC - Where Networking Futures Take Flight!"

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The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications.The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.

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International Journal of Computer Networks & Communications (IJCNC )is a open access journal that publishes articles which contribute new results in all areas Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & Communications. Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the Computer Networks & Communications.

International Journal of Computer Networks & Communications (IJCNC)

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The Science Gateways Community Institute (SGCI), opened in August 2016, provides free resources, services, experts, and ideas for creating and sustaining science gateways. It offers five areas of services to the science gateway developer and user communities: the Incubator, Extended Developer Support, the Scientific Software Collaborative, Community Engagement and Exchange, and Workforce Development. While all these areas are available to US-based communities, the Incubator, the Scientific Software Collaborative and the Community Engagement and Exchange serve also the international communities. We aim at reaching out and supporting beyond borders on international scale with diverse measures and our intent is to form and deepen collaborations with partner organizations and coalitions beneficial and/or related to the science gateways community. Research topics are independent of national borders and researchers spread worldwide can benefit from each other’s research results, software, data and from lessons learned — via online materials and publications or at international events. The gateway community has long benefitted from this type of exchange. This paper will present related work describing the benefits of international collaborations generally, and specifically as they relate to science gateways. We go into detail regarding SGCI’s ongoing work on international scale and its work planned in the near future.

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International Journal of Ubiquitous Computing (IJU) is a quarterly open access peer-reviewed journal provides excellent international forum for sharing knowledge and results in theory, methodology and applications of ubiquitous computing. Current information age is witnessing a dramatic use of digital and electronic devices in the workplace and beyond. Ubiquitous Computing presents a rather arduous requirement of robustness, reliability and availability to the end user. Ubiquitous computing has received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational applications in real life. The aim of the journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

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Students’ prompt to complete their degrees requires real time projects that showcase their potential and ability to apply knowledge they learn throughout courses. Some students desire to join research or academic oriented careers. Apart from working on the projects they required to pen down the research work in the form of articles so that others working in the field would know about the research developments and transmission of knowledge go on without hassle.

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The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications.The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.

Networks & Communications

Ricardo014

Currently, investments in research and development in Africa are about 0.6% of the global total of R&D investment, significantly lower than other regions. One of the foremost strategies to address this knowledge imbalance would be the packaging of African knowledge products in such a way that they are available and accessible on the internet. There is no doubt that Africans are producing lots of knowledge in their informal conversation as in formal engagements of varying types. This knowledge is being produced daily in villages and urban spaces, by African government officials and businesses, by students and researchers. Traditional healers are also applying indigenous knowledge to offer cures for COVID-19. Thus, the problem from an African perspective is less that of knowledge production and more one of the gathering, packaging and dissemination of the knowledge. This training present practical tools, platforms and strategies to effectively disseminate your research results to various stakeholders. It would help you make your research visible beyond academia and create more impact in society.

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Deadline : 31st May 2013 Notification : 15th June 2013 Revision : 20th June 2013 Publication : 01st July 2013 IJAET welcomes research articles from scientists, Engineers and Research Scholars involved in all areas of engineering and technology from all over the world to publish high quality research papers. Papers for publication in the IJAET are selected through rigorous peer review to ensure originality, timeliness, relevance, and readability. IJAET is inviting fresh submissions for its forthcoming issue, Volume 6, Issue 3. Authors are encouraged to contribute their original research to the journal by submitting their research papers that comes under any of following category: research findings, projects, literature surveys, review works, case studies, short communications of high quality, theoretical or empirical research articles, book reviews, proposals, analysis, tutorials, editorials as well as pedagogical and curricular issues that describe significant advances in field of Engineering and Technology.

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The integration of artificial intelligence technology with a scalable Internet of Things (IoT) platform facilitates diverse smart communication services, allowing remote users to access services from anywhere at any time. The multi-server environment within IoT introduces a flexible security service model, enabling users to interact with any server through a single registration. To ensure secure and privacy preservation services for resources, an authentication scheme is essential. Zhao et al. recently introduced a user authentication scheme for the multi-server environment, utilizing passwords and smart cards, claiming resilience against well-known attacks. This paper conducts cryptanalysis on Zhao et al.'s scheme, focusing on denial of service and privacy attacks, revealing a lack of user-friendliness. Subsequently, we propose a new multi-server user authentication scheme for privacy preservation with fuzzy commitment over the IoT environment, addressing the shortcomings of Zhao et al.'s scheme. Formal security verification of the proposed scheme is conducted using the ProVerif simulation tool. Through both formal and informal security analyses, we demonstrate that the proposed scheme is resilient against various known attacks and those identified in Zhao et al.'s scheme.

Multi-Server user Authentication Scheme for Privacy Preservation with Fuzzy C...

IJCNCJournal

In -Vehicle Ad-Hoc Network (VANET), vehicles continuously transmit and receive spatiotemporal data with neighboring vehicles, thereby establishing a comprehensive 360-degree traffic awareness system. Vehicular Network safety applications facilitate the transmission of messages between vehicles that are near each other, at regular intervals, enhancing drivers' contextual understanding of the driving environment and significantly improving traffic safety. Privacy schemes in VANETs are vital to safeguard vehicles’ identities and their associated owners or drivers. Privacy schemes prevent unauthorized parties from linking the vehicle's communications to a specific real-world identity by employing techniques such as pseudonyms, randomization, or cryptographic protocols. Nevertheless, these communications frequently contain important vehicle information that malevolent groups could use to Monitor the vehicle over a long period. The acquisition of this shared data has the potential to facilitate the reconstruction of vehicle trajectories, thereby posing a potential risk to the privacy of the driver. Addressing the critical challenge of developing effective and scalable privacy-preserving protocols for communication in vehicle networks is of the highest priority. These protocols aim to reduce the transmission of confidential data while ensuring the required level of communication. This paper aims to propose an Advanced Privacy Vehicle Scheme (APV) that periodically changes pseudonyms to protect vehicle identities and improve privacy. The APV scheme utilizes a concept called the silent period, which involves changing the pseudonym of a vehicle periodically based on the tracking of neighboring vehicles. The pseudonym is a temporary identifier that vehicles use to communicate with each other in a VANET. By changing the pseudonym regularly, the APV scheme makes it difficult for unauthorized entities to link a vehicle's communications to its real-world identity. The proposed APV is compared to the SLOW, RSP, CAPS, and CPN techniques. The data indicates that the efficiency of APV is a better improvement in privacy metrics. It is evident that the AVP offers enhanced safety for vehicles during transportation in the smart city.

Advanced Privacy Scheme to Improve Road Safety in Smart Transportation Systems

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April 2024 - Top 10 Read Articles in Computer Networks & Communications

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Malware is one of the threats to security of computer networks and information systems. Since malware instances are available sufficiently, there is increased interest among researchers on usage of Artificial Intelligence (AI). Of late AI-enabled methods such as machine learning (ML) and deep learning paved way for solving many real-world problems. As it is a learning-based approach, accumulated training samples help in improving thequality of training and thus leveraging malware detection accuracy. Existing deep learning methods are focusing on learning-based malware detection systems. However, there is need for improving the state of the art through ensemble approach. Towards this end, in this paper we proposed a framework known as Deep Ensemble Framework (DEF) for automatic malware detection. The framework obtains features from training samples. From given malware instance a grayscale image is generated. There is another process to extract the opcode sequences. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) techniques are used to obtain grayscale image and opcode sequence respectively. Afterwards, a stacking ensemble is employed in order to achieve efficient malware detection and classification. Malware samples collected fromthe Internet sources and Microsoft are used for theempirical study. An algorithm known as Ensemble Learning for Automatic Malware Detection (EL-AML) is proposed to realize our framework. Another algorithm named Pre-Process is proposed to assist the EL-AML algorithm for obtaining intermediate features required by CNN and LSTM.Empirical study reveals that our framework outperforms many existing methods in terms of speed-up and accuracy.

DEF: Deep Ensemble Neural Network Classifier for Android Malware Detection

IJCNCJournal

With the emergence of smart devices and the Internet of Things (IoT), millions of users connected to the network produce massive network traffic datasets. These vast datasets of network traffic, Big Data are challenging to store, deal with and analyse using a single computer. In this paper we developed parallel implementation using a High Performance Computer (HPC) for the Non-Negative Matrix Factorization technique as an engine for an Intrusion Detection System (HPC-NMF-IDS). The large IoT traffic datasets of order of millions samples are distributed evenly on all the computing cores for both storage and speedup purpose. The distribution of computing tasks involved in the Matrix Factorization takes into account the reduction of the communication cost between the computing cores. The experiments we conducted on the proposed HPC-IDS-NMF give better results than the traditional ML-based intrusion detection systems. We could train the HPC model with datasets of one million samples in only 31 seconds instead of the 40 minutes using one processor), that is a speed up of 87 times. Moreover, we have got an excellent detection accuracy rate of 98% for KDD dataset.

High Performance NMF Based Intrusion Detection System for Big Data IOT Traffic

IJCNCJournal

So far, Wireless Body Area Networks (WBANs) have played a pivotal role in driving the development of intelligent healthcare systems with broad applicability across various domains. Each WBAN consists of one or more types of sensors that can be embedded in clothing, attached directly to the body, or even implanted beneath an individual's skin. These sensors typically serve asingle application. However, the traffic generated by each sensor may have distinct requirements. This diversity necessitates a dual approach: tailored treatment based on the specific needs of each traffic typeand the fulfillment of application requirements, such asreliability and timeliness. Never the less, the presence of energy constraints and the unreliable nature of wireless communications make QoS provisioning under such networks a non-trivial task. In this context, the current paper introduces a novel Medium AccessControl (MAC) strategy for the regular traffic applications of WBANs, designed to significantly enhance efficiency when compared to the established MAC protocols IEEE 802.15.4 and IEEE 802.15.6, with a particular focus on improving reliability, timeliness, and energy efficiency.

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The efficient use of energy in wireless sensor networks is critical for extending node lifetime. The network topology is one of the factors that have a significant impact on the energy usage at the nodes and the quality of transmission (QoT) in the network. We propose a topology control algorithm for software-defined wireless sensor networks (SDWSNs) in this paper. Our method is to formulate topology control algorithm as a nonlinear programming (NP) problem with the objective to optimizing two metrics, maximum communication range, and desired degree. This NP problem is solved at the SDWSN controller by employing the genetic algorithm (GA) to determine the best topology. The simulation results show that the proposed algorithm outperforms the MaxPower algorithm in terms of average node degree and energy expansion ratio.

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High Performance NMF based Intrusion Detection System for Big Data IoT Traffic

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Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.

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IJCNCJournal

IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate networking. Internet of Things (IoT) networks have a lot of control overhead messages because devices are mobile. These signals are generated by the constant flow of control data as such device identity, geographical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead communication management method. Many cluster-based routing methods have been developed to address system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This will facilitate the secure and reliable interchange of healthcare data between professionals and patients. NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option from a range of choices and preparing for likely outcomes problem addressing limitations on activities is a primary focus during the review process. Predictive inquiry employs the process of analyzing data to forecast and anticipate future events. This document provides an explanation of compact units. The Predictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a routing information table for each intelligent node, resulting in higher routing performance. Both principal and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outperform CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition, the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.

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The Optimal Link State Routing (OLSR) protocol employs multipoint relay (MPR) nodes to disseminate topology control (TC) messages, enabling network topology discovery and maintenance. However, this approach increases control overhead and leads to wasted network bandwidth in stable topology scenarios due to fixed flooding periods. To address these challenges, this paper presents an Efficient Topology Maintenance Algorithm (ETM-OLSR) for Enhanced Link-State Routing Protocols. By reducing the number of MPR nodes, TC message generation and forwarding frequency are minimized. Furthermore, the algorithm selects a smaller subset of TC messages based on the changes in the MPR selection set from the previous cycle, adapting to stable and fluctuating network conditions. Additionally, the sending cycle of TC messages is dynamically adjusted in response to network topology changes. Simulation results demonstrate that the ETM-OLSR algorithm effectively reduces network control overhead, minimizes end-to-end delay, and improves network throughput compared to traditional OLSR and HTR-OLSR algorithms.

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Recently, to deliver services directly to the network edge, fog computing, an emerging and developing technology, acts as a layer between the cloud and the IoT worlds. The cloud or fog computing nodes could be selected by IoTs applications to meet their resource needs. Due to the scarce resources of fog devices that are available, as well as the need to meet user demands for low latency and quick reaction times, resource allocation in the fog-cloud environment becomes a difficult problem. In this problem, the load balancing between several fog devices is the most important element in achieving resource efficiency and preventing overload on fog devices. In this paper, a new adaptive resource allocation technique for load balancing in a fog-cloud environment is proposed. The proposed technique ranks each fog device using hybrid multi-criteria decision- making approaches Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS), then selects the most effective fog device based on the resulting ranking set. The simulation results show that the proposed technique outperforms existing techniques in terms of load balancing, response time, resource utilization, and energy consumption. The proposed technique decreases the number of fog nodes by 11%, load balancing variance by 69% and increases resource utilization to 90% which is comparatively higher than the comparable methods.

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Cloud computing is the quick, simple, and economical distribution of computing services. To offer quality services in cloud infrastructure, the NFV Management and Orchestration (MANO) function is a critical task for managing both infrastructure resources and network functions. Since the demand for cloud services is rapidly increasing, cloud service providers are constantly trying to reduce operational costs. Hence the elements of MANO functions have gained critical importance in cloud infrastructure. To use MANO efficiently, there are hypervisors (OpenStack) that act as a middle layer between hardware and software that are used to provide Infrastructure-as-a-Service solutions through a set of interrelated services. These services are managed by an application programming interface (API). The existing orchestration method uses Representational state transfer (REST) API for communicating with core components of a hypervisor. These API have been integral part of creating and deploying a server. To improve the performance, this paper proposes a novel approach for orchestration based on the open-source remote procedure call framework, known as gRPC. To analyse the proposed solution, both REST and gRPC-based orchestration methods are implemented for creating and launching servers using OpenStack cloud computing platform. The server creation time is obtained for different scenarios and SFC use cases. The results show that by using gRPC, the performance in terms of server creation time of an orchestration is improved by up to 27% compared to the traditional REST-based orchestration.

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IJCNCJournal

Cognitive radio (CR) is a promising technology for both present and future telecommunications to satisfy the demand of the next generation due to inefficient use of the allocated spectrum. Due to its ability to utilize the available bandwidth of other wireless communication networks and so enhance its occupancy. Spectrum sensing (SS) is the key characteristic of the CR system that helps it identify the empty spectrum. SS has gained a lot of interest recently and it is an active research area since it offers additional opportunities to secondary users. A broad variety of analytical methods to identify the Primary User's (PUs) presence have emerged as a result of SS techniques for CRs. Although each approach has its own benefits, the drawbacks attached to them make an individual implementation of the technique impractical for usage. To mitigate the drawbacks and maximize the benefits offered by the individual methods, a two stage detector can be employed for SS. However, the stages method lengthens the time needed to sense the spectrum and produce a definitive result. In this paper, we propose a two-stage sensing approach, where the first stage is an Interval Dependent Denoising detector (IDD) and followed by a second stage is Energy Detection (ED) which provides a large decrease in the mean sensing time compared to different related two-stage SS approaches The ED method is simple and has a short sensing time, but it performs poorly when the Signal to Noise Ratio (SNR) is low, Hence it is thought that separating PU activity from noise is essential for accurate SS. Therefore, we use IDD as a noise reduction technique in the first stage before the ED stage. The simulation results have been utilized to demonstrate that this technique leads to a large savings in sensing time as compared to an existing two-stage detection approaches

Sensing Time Improvement Using Two Stage Detectors for Cognitive Radio System

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