Journal of Network Security Computer Networks
EISSN : 2581-639X
Published by: MAT Journals (A unit of ARV Infomedia Pvt. Ltd.) (10.46610)
Total articles ≅ 16
Latest articles in this journal
Published: 28 September 2022
Journal of Network Security Computer Networks, Volume 8, pp 9-17; https://doi.org/10.46610/jonscn.2022.v08i03.002
Distributed massive MIMO is an arising answer for improve 5G limit, throughput, and QoE inside. There are various ways to advance indoor execution. The indoor cell network should have totally different qualities and needs from its outside partner, yet a consistent encounter should be kept up with while moving between the two. New arrangements, like small cells and distributed antenna systems (DAS), have been added to every portable age to work on the presentation and quality of experience (QoE) for indoor clients. Notwithstanding, the goal lines are moved with each new age of purpose cases, and, surprisingly, more significant levels of execution and QoE are required. The improvement in the indoor experience needed in the 5G period should be extensive. The 5G guidelines empower the organizations to help tremendous quantities of clients or associated gadgets in little spaces (up to 1 million for each square kilometer), while consuming information at multi-gigabit speeds and with low idleness and high unwavering quality. This paper give a brief review on improvement in channel capacity and QoE using massiveness of MIMO technology for 5G environment in distributed manner.
Published: 28 September 2022
Journal of Network Security Computer Networks, Volume 8, pp 1-8; https://doi.org/10.46610/jonscn.2022.v08i03.001
As of late presented 5G New Radio is the primary remote standard intended to help basic and massive machine-type interchanges (m-MTC). Notwithstanding, it is previously becoming apparent that a portion of the additional requesting prerequisites for MTC cannot be completely upheld by 5G organizations. Close by, arising use cases and applications towards 2030 will lead to new and more rigid necessities on remote network and MTC specifically. This new age of portable correspondence frameworks will in general turn into an innovation stage that will empower the improvement of new applications, plans of action, and ventures, for example, huge machine-type correspondences. This will be conceivable through making a satisfactory environment that could give monstrous machine-type correspondence utilizing a solitary stage in light of the Internet of Things (IoT) idea. This paper reflects the brief importance of massiveness of machine type communication in 5G eras with challenges and other important issues.
Published: 9 August 2022
Journal of Network Security Computer Networks, Volume 8, pp 53-58; https://doi.org/10.46610/jonscn.2022.v08i02.005
Let us take a look at these new high-tech communications and how they shape the industry. Local area-defined software is a software system that manages wide area networks, provides ease of use, single site management and minimizes the cost, and can enhance communication with branch offices and the cloud. There are been some changes in local networks nothing has been more relevant than software-defined WAN or SD-WAN, which changes the way network professionals think about improving multimedia connectivity such as changing the Multi-protocol label. (MPLS), independent transfer and Digital Subscriber Line (DSL). The separation of hardware and control plane is similar to how software-defined networks use visual technology to improve the management and operation of their data centers. In fact, SD-WAN are set up and managed using proprietary protocols such as Cisco iOS. That is less hardware and less control over the hardware. A leading SD-WAN application that enables organizations to build highly efficient WANs with cost-effective turnkey Internet access. This allows organizations to phase out or completely replace low-cost WAN communication technologies such as MPLS. So customers can easily manage their network regardless of the connection provider. SD-WAN is currently one of the hottest topics with a real impact on CC services and WAN environment. SD-WAN it affects the way we think about how we use the web services yet. More importantly, it has great potential to change the way we use Communication Services in future. There are several industries that are interesting SD-WAN deployment conditions.
Published: 8 July 2022
Journal of Network Security Computer Networks, Volume 8, pp 46-52; https://doi.org/10.46610/jonscn.2022.v08i02.004
In this paper, we overview the latest benefits of honeypots. Few outstanding plans and their analysis is specified. The facts related to honeypots in schooling and hybrid surroundings with the Intrusion Detection System were specified. In this paper, we specify the use of the signature approach in honeypots for visitor analysis. In this, we summarize all these features. Containerization encourages a few functions to run machine kernel that is Name spaces and control organizations. These are some Linux kernel capabilities that permit separation of process and whilst these kernel functions had been addressed one after the other with the purpose to develop lightweight, OS-level virtualization, Docker changed into evolved.
Published: 10 January 2022
Journal of Network Security Computer Networks, Volume 8, pp 1-17; https://doi.org/10.46610/jonscn.2022.v08i01.001
Intrusion Detection Systems (IDS) is one of the important aspects of cyber security that can detect the anomalies in the network traffic. IDS are a part of Second defense line of a system that can be deployed along with other security measures such as access control, authentication mechanisms and encryption techniques to secure the systems against cyber-attacks. However, IDS suffers from the problem of handling large volume of data and in detecting zero-day attacks (new types of attacks) in a real-time traffic environment. To overcome this problem, an intelligent Deep Learning approach for Intrusion Detection is proposed based on Convolutional Neural Network (CNN-IDS). Initially, the model is trained and tested under a new real-time traffic dataset, CSE-CIC-IDS 2018 dataset. Then, the performance of CNN-IDS model is studied based on three important performance metrics namely, accuracy / training time, detection rate and false alarm rate. Finally, the experimental results are compared with those of various Deep Discriminative models including Recurrent Neural network (RNN), Deep Neural Network (DNN) etc., proposed for IDS under the same dataset. The Comparative results show that the proposed CNN-IDS model is very much suitable for modelling a classification model both in terms of binary and multi-class classification with higher detection rate, accuracy, and lower false alarm rate. The CNN-IDS model improves the accuracy of intrusion detection and provides a new research method for intrusion detection.
Published: 29 October 2021
Journal of Network Security Computer Networks, Volume 7; https://doi.org/10.46610/jonscn.2021.v07i03.005
Mankind is confronting these days a histrionic pandemic scene with the Coronavirus proliferation over all continents. The Covid-19 pandemic outbreak is as yet not very much portrayed, and numerous research teams everywhere on the world are chipping away at one or the other restorative therapeutic issues or immunization issues. The outburst of COVID-19 has constituted a danger to wellbeing of world. With the expanding number of individuals tainted, medical services frameworks, particularly those in economically emerging nations, are bearing gigantic pressing factor for the devising a prognostic model. There is a dire requirement for the analysis of COVID-19 and the anticipation of inpatients. To diminish these issues, a data statistical information driven clinical aid framework is advanced in this paper. In view of two real world datasets in Wuhan, China, the proposed framework coordinates information from various sources with tools of Machine Learning (ML) to anticipate COVID-19 tainted likelihood of suspected patients in their first visit, and afterward foresee mortality of affirmed cases. As opposed to picking an interpretable calculation, this framework isolates the clarifications from ML models. It can do help to patient triaging and give some valuable guidance to specialists and doctors. A prognosis model is in the way of extraordinary premium for specialists to adjust their consideration methodology for therapeutic or diagnosis procedure.
Published: 23 September 2021
Journal of Network Security Computer Networks, Volume 7; https://doi.org/10.46610/jonscn.2021.v07i03.003
The internet is taking component in a developing feature in every non-public and professional activity. The real-time, delay sensitive and mission-essential purposes, community availability requirement is beforehand for internet carrier providers (ICPs). The loop-loose criterion (LLC) approach has been extensively deployed through numerous ICPs for handling the best network component failure state of affairs in fantastic internet through. The achievement of LLC lies in its inherent simplicity; however, this comes at the rate of letting certain failure. To reap complete failure safety with LLC without incurring significant extra, a singular link protection scheme, hybrid hyperlink protocol (HLP), to reap failure routing. In contrast with in advance schemes, HLP guarantees tall network in a greater surroundings pleasant way. HLP is carried out in stages. Initial level substances a surroundings pleasant LLC primarily based totally on (MNP-e). The complexity of the set of rules is decrease than that of Dijkstra’s set of rules and might gift similar to network availability with LCC (Loop-loose criterion). Moment level substances backup direction safety based on MNP-e, the area totally a minimum type of need to be protected, to fulfill the network requirement. We don't forget those algorithms in a massive spread of associated, real and actual, and the effects display that HLP can achieve lofty network without introducing apparent.
Published: 22 September 2021
Journal of Network Security Computer Networks, Volume 7; https://doi.org/10.46610/jonscn.2021.v07i03.002
The advancement of cloud and IoT technologies, has made network administration more difficult. Software-Defined Networking is one of the trending technologies which replaces the traditional networking domain with the programmable network configuration. In the current development of the network architecture, data security plays a prominent role. Many strategies for dealing with network attacks have been developed, among them deep learning is one of the most advanced technology. The paper aims to classify the network traffic into normal traffic and attack traffic with Multilayer Perceptron (MLP). The simulation uses a python programming language with many packages like Numpy, sci-kit, seaborn, etc. in a mininet SDN test bed with the Ryu controller. From the obtained results proposed algorithm gives better accuracy for classifying the attack traffic and normal traffic in the network.
Published: 6 July 2021
Journal of Network Security Computer Networks, Volume 7; https://doi.org/10.46610/jonscn.2021.v07i02.004
Sign language is the only way of method to communication for hearing impaired and deaf-dumb peoples. The system will recognize the signs between signers and non-signers, this will give the meaning of sign. The proposed method is helpful for the people who have hearing difficulties and in general who use very simple and effective method is sign language. This system can be used for converting sign language to text using CNN approach. An image capture system is used for sign language conversion. It captures the signs and display on the screen as writing. Results prove that the planned methodology for sign detection is more effective and has high accuracy. Experimental results will acknowledge the signs that the planned system is 80% accuracy.
Published: 30 June 2021
Journal of Network Security Computer Networks, Volume 7; https://doi.org/10.46610/jonscn.2021.v07i02.003
Intrusion detection systems (IDS) play a critical role in network security by monitoring network traffic for malicious activities and detecting vulnerability exploits against target applications or computers. A large number of redundant and irrelevant features increase the dimensionality of the dataset, which increases the computational overhead on the system and reduces its performance. This paper studies different filter-based feature selection techniques to improve performance of system. Feature selection techniques are used to select a well performing subset of features followed by technique of ensemble learning, which selects an optimal subset of features by combining multiple subsets of features. Feature selection combined with ensemble learning is explored in this paper. The performance of the algorithms implemented in existing research in terms of accuracy, false alarm rates, and true positive rates is explored, and their shortcomings are observed.