International Journal of Computer Network and Information Security (IJCNIS)

ISSN: 2074-9090 (Print)

ISSN: 2074-9104 (Online)

DOI: https://doi.org/10.5815/ijcnis

Website: https://www.mecs-press.org/ijcnis

Published By: MECS Press

Frequency: 6 issues per year

Number(s) Available: 129

ICV: 2014 8.19

SJR: 2021 0.438

(IJCNIS) in Google Scholar Citations / h5-index

IJCNIS is committed to bridge the theory and practice of computer network and information security. From innovative ideas to specific algorithms and full system implementations, IJCNIS publishes original, peer-reviewed, and high quality articles in the areas of computer network and information security. IJCNIS is well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of computer network, information security, and their applications.

 

IJCNIS has been abstracted or indexed by several world class databases: ScopusSCImago, Google Scholar, Microsoft Academic Search, CrossRef, Baidu Wenku, IndexCopernicus, IET Inspec, EBSCO, VINITI, JournalSeek, ULRICH's Periodicals Directory, WorldCat, Scirus, Academic Journals Database, Stanford University Libraries, Cornell University Library, UniSA Library, CNKI Scholar, ProQuest, J-Gate, ZDB, BASE, OhioLINK, iThenticate, Open Access Articles, Open Science Directory, National Science Library of Chinese Academy of Sciences, The HKU Scholars Hub, etc..

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IJCNIS Vol. 16, No. 1, Feb. 2024

REGULAR PAPERS

Machine Learning-based Intrusion Detection Technique for IoT: Simulation with Cooja

By Ali H. Farea Kerem Kucuk

DOI: https://doi.org/10.5815/ijcnis.2024.01.01, Pub. Date: 8 Feb. 2024

The Internet of Things (IoT) is one of the promising technologies of the future. It offers many attractive features that we depend on nowadays with less effort and faster in real-time. However, it is still vulnerable to various threats and attacks due to the obstacles of its heterogeneous ecosystem, adaptive protocols, and self-configurations. In this paper, three different 6LoWPAN attacks are implemented in the IoT via Contiki OS to generate the proposed dataset that reflects the 6LoWPAN features in IoT. For analyzed attacks, six scenarios have been implemented. Three of these are free of malicious nodes, and the others scenarios include malicious nodes. The typical scenarios are a benchmark for the malicious scenarios for comparison, extraction, and exploration of the features that are affected by attackers. These features are used as criteria input to train and test our proposed hybrid Intrusion Detection and Prevention System (IDPS) to detect and prevent 6LoWPAN attacks in the IoT ecosystem. The proposed hybrid IDPS has been trained and tested with improved accuracy on both KoU-6LoWPAN-IoT and Edge IIoT datasets. In the proposed hybrid IDPS for the detention phase, the Artificial Neural Network (ANN) classifier achieved the highest accuracy among the models in both the 2-class and N-class. Before the accuracy improved in our proposed dataset with the 4-class and 2-class mode, the ANN classifier achieved 95.65% and 99.95%, respectively, while after the accuracy optimization reached 99.84% and 99.97%, respectively. For the Edge IIoT dataset, before the accuracy improved with the 15-class and 2-class modes, the ANN classifier achieved 95.14% and 99.86%, respectively, while after the accuracy optimized up to 97.64% and 99.94%, respectively. Also, the decision tree-based models achieved lightweight models due to their lower computational complexity, so these have an appropriate edge computing deployment. Whereas other ML models reach heavyweight models and are required more computational complexity, these models have an appropriate deployment in cloud or fog computing in IoT networks.

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Comparative Risk Assessment of Cyber Threats Based on Average and Fuzzy Sets Theory

By Oleksandr Evgeniyovych Korystin Oleksandr Korchenko Svitlana Kazmirchuk Serhii Demediuk Oleksandr Oleksandrovych Korystin

DOI: https://doi.org/10.5815/ijcnis.2024.01.02, Pub. Date: 8 Feb. 2024

Applied results of scientific analysis should be the key focus of modern security research. A comparative analysis of research results obtained using different methods, as an applied task, forms a broader basis for interpreting the results and substantiating the conclusions. A social survey and expert opinion research were conducted to implement the general concept of strategic analysis of cybersecurity in Ukraine. Using the method based on determining the average value in a certain set of estimates, as well as the method based on the theory of fuzzy sets, the risks of spreading certain cyber threats in Ukraine were assessed. The results were compared. Although the use of different measurement methods led to some differences in quantitative risk indicators, the comparative analysis of the ratio of the level of different cyber threats did not change significantly. At the same time, the fuzzy set method provided more flexible interpretation of the results to characterize cyber threats in terms of their upward or downward trend. In general, the combined approach to cyber threat risk assessment can become an important risk management tool, as it takes advantage of different methods and allows for a deeper understanding of the current situation and the formation of more informed management decisions.

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Hybrid Spider Monkey Optimization Mechanism with Simulated Annealing for Resource Provisioning in Cloud Environment

By A. Archana N. Kumar Mohammad Zubair Khan

DOI: https://doi.org/10.5815/ijcnis.2024.01.03, Pub. Date: 8 Feb. 2024

Cloud computing is an emerging concept that makes better use of a large number of distributed resources. The most significant issue that affects the cloud computing environment is resource provisioning. Better performance in the shortest amount of time is an important goal in resource provisioning. Create the best solution for dynamically provisioning resources in the shortest time possible. This paper aims to perform resource provisioning with an optimal performance solution in the shortest time. Hybridization of two Meta-heuristics techniques, such as HSMOSA (Hybrid Spider Monkey Optimization with Simulated Annealing), is proposed in resource provisioning for cloud environment. Finds the global and local value using Spider Monkey Optimization's (SMO) social behavior and then utilizes Simulated Annealing (SA) to search around the global value in each iteration. As a result, the proposed approach aids in enhancing their chances of improving their position. The CloudSimPlus Simulator is used to test the proposed approach. The fitness value, execution time, throughput, mean, and standard deviation of the proposed method were calculated over various tasks and execution iterations. These performance metrics are compared with the PSO-SA algorithm. Simulation results validate the better working of the proposed HSMOSA algorithm with minimum time compared to the PSO-SA algorithm.

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Secure Access of Folders and Files after Removal of Duplicacy over the Cloud

By Deepika Gautam Suvendir Rimer Vipin Saxena

DOI: https://doi.org/10.5815/ijcnis.2024.01.04, Pub. Date: 8 Feb. 2024

Cloud Computing has been the most popular approach of computing due to faster access to folders and files at a low cost. Hence, many organizations are shifting the old long database folders and files over the cloud which may be text, audio, video or in the other formats. Due to large size of the database with multiple storages of folders and files over the cloud, there may be chances of duplicate access of the database folders and files which may cause the loss of time of execution or accessing the database files. In the present work, a technique is developed to remove duplicate files in the form of .txt, .doc, .jpg, .pdf as well as duplicate folders after applying a well-known ElGamal algorithm later on converted as fuzzy ElGamal technique, for faster retrieval of files in a very secure manner. For this purpose, Unified Modelling Language (UML) model is developed which has been implemented through Python programming language. The computed results towards the model’s efficiency have been depicted through tables and graphs, on a large database in the form of folders and files of Indian railway reservation system. The present work is significant for the large organizations and also useful for the users working over the cloud for faster accessing of the folders and files.

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Method of Performing Operations on the Elements of GF(2m) Using a Sparse Table

By Ivan Dychka Mykola Onai Andrii Severin Cennuo Hu

DOI: https://doi.org/10.5815/ijcnis.2024.01.05, Pub. Date: 8 Feb. 2024

For the implementation of error-correcting codes, cryptographic algorithms, and the construction of homomorphic methods for privacy-preserving, there is a need for methods of performing operations on elements GF(2m) that have low computational complexity. This paper analyzes the existing methods of performing operations on the elements GF(2m) and proposes a new method based on the use of a sparse table of elements of this field. The object of research is the processes of operations in information security systems. The subject of research is methods and algorithms for performing operations on elements GF(2m). The purpose of this research is to develop and improve methods and algorithms for performing operations on elements GF(2m) to reduce their computational complexity. Empirical methods and methods of mathematical and software modeling are used in the research. Existing and proposed algorithms are implemented using the C# programming language in the Visual Studio 2015 development environment. Experimental research of existing and developed algorithms was carried out according to the proposed method, which allows to level the influence of additional parameters on the results of the research. The conducted research on methods for performing operations on the elements GF(2m) shows the expediency of using a sparse table of field elements. This approach makes it possible to reduce the amount of RAM required for the software and hardware implementation of the developed method compared to the classical tabular method, which requires storage of a full table of correspondence of the polynomial and index representation of the field elements. In addition, the proposed method gives an increase in speed of more than 4 times for the operations of calculating the multiplicative inverse element and exponentiation. As a result, the proposed method allows to reduce the computational complexity of error-correcting codes, cryptographic algorithms, and the homomorphic methods for privacy-preserving.

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Energy Consumption-sensitive Intentional Rerouting of Protected Connections in Elastic Optical Networks

By Nogbou Georges ANOH Ali Ouattara KOBENAN Joel Christian ADEPO Michel BABRI Ahmed Dooguy KORA

DOI: https://doi.org/10.5815/ijcnis.2024.01.06, Pub. Date: 8 Feb. 2024

The reduction of energy consumption in elastic optical networks is of major interest to the research community. As a result, several methods for solving this problem in combination with existing classical problems have been proposed. Elastic optical networks are subject to disturbance phenomena that degrade their quality and performance. To optimize resources, operators must recalculate new routes and plan the displacement of established connections towards these new routes to cope with these phenomena, it’s the reconfiguration. The problem addressed in this article is to reconfigure a set of unicast protected connections without interruption to a new routing calculated during the process. Knowing that the use of backup paths solves the interruption problem, but has an impact on the overall energy consumption, the goal is to find a good compromise between the two sub-problems when switching from old routes to new ones. To the best of our knowledge, there is no work on reconfiguration that uses energy-aware backup paths. In this work, we proposed an energy-aware EERA_EON rerouting algorithm using the backup paths. Simulations have shown the performance of this approach in terms of energy consumption compared to the work of our predecessors. Subsequently, we proposed a classical BRA_EON rerouting algorithm in elastic optical networks. Simulation results show that we perform BRA_EON in terms of the number of steps.

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Container-to-fog Service Integration using the DIS-LC Algorithm

By Aruna. K. Pradeep. G.

DOI: https://doi.org/10.5815/ijcnis.2024.01.07, Pub. Date: 8 Feb. 2024

Containers have newly emerged as a potential way to encapsulate and execute programs. In contrast to virtual machines, each container does not have its own kernel and instead shares the host systems. Containers on the other hand are more lightweight, need fewer data to be sent between network nodes and boot up faster than VM. This makes containers a feasible choice, particularly for hosting and extending the services across the fog computing architecture. The major purpose of this paper is to describe the Distributed Intelligent Scheduling based Lightweight Container algorithm (DIS-LC), which is a revolutionary way for container to fog-services integration and resource optimization. In this proposed algorithm is compared to the least connection algorithm, round-robin algorithm and Ant Colony Optimization-based Light Weight Container (ACO-LWC). Operating cost and traffic cost are used to validate the suggested algorithm. Fog node running costs are divided into two categories: CPU and memory. When compared to current algorithms, quantitative research demonstrates that the proposed DIS-LC scheme gets the greatest performance in terms of all metrics. This demonstrate the algorithm is efficient. Finally, the performance of containerized services and resource management systems is evaluated using the iFogSim simulator.

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Fault Tolerance Exploration and SDN Implementation for de Bruijn Topology based on betweenness Coefficient

By Artem Volokyta Heorhii Loutskii Oleksandr Honcharenko Oleksii Cherevatenko Volodymyr Rusinov Yurii Kulakov Serhii Tsybulia

DOI: https://doi.org/10.5815/ijcnis.2024.01.08, Pub. Date: 8 Feb. 2024

This article considers the method of analyze potentially vulnerable places during development of topology for fault-tolerant systems based on using betweenness coefficient. Parameters of different topological organizations using De Bruijn code transformation are observed. This method, assessing the risk for possible faults, is proposed for other topological organizations that are analyzed for their fault tolerance and to predict the consequences of simultaneous faults on more significant fragments of this topology.

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An Enhancement of Identity Based Conditional Privacy-preserving Authentication Process in Vehicular Ad Hoc Networks

By K. Lakshmi Narayanan R. Naresh

DOI: https://doi.org/10.5815/ijcnis.2024.01.09, Pub. Date: 8 Feb. 2024

In general, Vehicular Ad hoc Networks (VANETs) are permitting the communication between one vehicle with neighboring vehicles, infrastructure, and Road-Side Unit (RSU). In this, vehicle platoon is commonly known as the vehicle driving pattern it categorizes the batching of the vehicle in the on the trot fashion. It has been reviewed as an effective resolution to mitigate the reduction in traffic blockage and to widen the opulence of the travel. However, the malicious activities of any unauthorized person in VANET are increased the damage to authorized vehicles. In this manuscript, the Identity based Conditional Privacy-Preserving Authentication (ID-CPPA) signature scheme is proposed to detect the malignant command vehicle very efficiently by the consumer vehicle. In this, the proposed ID-CPPA method uses one-way hash functions for improving the efficiency of Road-Side Unit (RSU) signing and verification of a messages. In order to provide better concealment to the vehicle, Phase Truncated Fourier Transform based asymmetric encryption algorithm (PTFT-AE) is proposed. Thus, the proposed ID-CPPA-PTFT-AE approach has achieved 28.96%, 37.58%, 31.36% higher security rate and 25.8%, 37.9%, 42.6% lower delay than the existing MPDC-LPNS, PPSR-GS, and WCAA-TST methods respectively.

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Auto-metric Graph Neural Network based Blockchain Technology for Secured Dynamic Optimal Routing in MANET

By Francis H. Shajin Muthusamy Palaniappan P. Rajesh

DOI: https://doi.org/10.5815/ijcnis.2024.01.10, Pub. Date: 8 Feb. 2024

Mobile ad hoc network (MANET) routing is a generous tactic used for allocating packets to the base station (BS). During the operations of routing, occurrence of malicious node embellishes the mobile ad hoc network operations. For that reason, a trusted distributed routing protocol is obliged that maintains the routing buttressing and the proficiency of mobile ad hoc network. To overcome these challenging issues, Auto-Metric Graph Neural Network based Blockchain technology is proposed in this manuscript for Secured Dynamic Optimal Routing in MANET (BC-SDOR-MANET-AGNN). The proposed approach is simulated in NS-2 tool. The proposed BC-SDOR-MANET-AGNN approach attains 76.26%, 65.57%, 42.9% minimal delay during 25% malicious routing environment, 73.06%, 63.82%, 38.84% less delay during 50% malicious routing environment when analyzed to the existing models, like enhanced hybrid secure multipath routing protocol for MANET (BC-SDOR-MANET-GAHC), an improved ad hoc on-demand distance vector routing security approach based on BC technology in MANET (BC-SDOR-MANET-AODV-MQS) and block chain-based better approach for the mobile ad-hoc networking protocol using ensemble algorithm (BC-SDOR-MANET-E-BATMAN) methods.

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D2D Communication Using Distributive Deep Learning with Coot Bird Optimization Algorithm

By Nethravathi H. M. Akhila S. Vinayakumar Ravi

DOI: https://doi.org/10.5815/ijcnis.2023.05.01, Pub. Date: 8 Oct. 2023

D2D (Device-to-device) communication has a major role in communication technology with resource and power allocation being a major attribute of the network. The existing method for D2D communication has several problems like slow convergence, low accuracy, etc. To overcome these, a D2D communication using distributed deep learning with a coot bird optimization algorithm has been proposed. In this work, D2D communication is combined with the Coot Bird Optimization algorithm to enhance the performance of distributed deep learning. Reducing the interference of eNB with the use of deep learning can achieve near-optimal throughput. Distributed deep learning trains the devices as a group and it works independently to reduce the training time of the devices. This model confirms the independent resource allocation with optimized power value and the least Bit Error Rate for D2D communication while sustaining the quality of services. The model is finally trained and tested successfully and is found to work for power allocation with an accuracy of 99.34%, giving the best fitness of 80%, the worst fitness value of 46%, mean value of 6.76 and 0.55 STD value showing better performance compared to the existing works.

[...] Read more.
Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN

By Ramesh Vatambeti Vijay Kumar Damera Karthikeyan H. Manohar M. Sharon Roji Priya C. M. S. Mekala

DOI: https://doi.org/10.5815/ijcnis.2023.06.01, Pub. Date: 8 Dec. 2023

Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection.

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A Critical appraisal on Password based Authentication

By Amanpreet A. Kaur Khurram K. Mustafa

DOI: https://doi.org/10.5815/ijcnis.2019.01.05, Pub. Date: 8 Jan. 2019

There is no doubt that, even after the development of many other authentication schemes, passwords remain one of the most popular means of authentication. A review in the field of password based authentication is addressed, by introducing and analyzing different schemes of authentication, respective advantages and disadvantages, and probable causes of the ‘very disconnect’ between user and password mechanisms. The evolution of passwords and how they have deep-rooted in our life is remarkable. This paper addresses the gap between the user and industry perspectives of password authentication, the state of art of password authentication and how the most investigated topic in password authentication changed over time. The author’s tries to distinguish password based authentication into two levels ‘User Centric Design Level’ and the ‘Machine Centric Protocol Level’ under one framework. The paper concludes with the special section covering the ways in which password based authentication system can be strengthened on the issues which are currently holding-in the password based authentication.

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Statistical Techniques for Detecting Cyberattacks on Computer Networks Based on an Analysis of Abnormal Traffic Behavior

By Zhengbing Hu Roman Odarchenko Sergiy Gnatyuk Maksym Zaliskyi Anastasia Chaplits Sergiy Bondar Vadim Borovik

DOI: https://doi.org/10.5815/ijcnis.2020.06.01, Pub. Date: 8 Dec. 2020

Represented paper is currently topical, because of year on year increasing quantity and diversity of attacks on computer networks that causes significant losses for companies. This work provides abilities of such problems solving as: existing methods of location of anomalies and current hazards at networks, statistical methods consideration, as effective methods of anomaly detection and experimental discovery of choosed method effectiveness. The method of network traffic capture and analysis during the network segment passive monitoring is considered in this work. Also, the processing way of numerous network traffic indexes for further network information safety level evaluation is proposed. Represented methods and concepts usage allows increasing of network segment reliability at the expense of operative network anomalies capturing, that could testify about possible hazards and such information is very useful for the network administrator. To get a proof of the method effectiveness, several network attacks, whose data is storing in specialised DARPA dataset, were chosen. Relevant parameters for every attack type were calculated. In such a way, start and termination time of the attack could be obtained by this method with insignificant error for some methods.

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Social Engineering: I-E based Model of Human Weakness for Attack and Defense Investigations

By Wenjun Fan Kevin Lwakatare Rong Rong

DOI: https://doi.org/10.5815/ijcnis.2017.01.01, Pub. Date: 8 Jan. 2017

Social engineering is the attack aimed to manipulate dupe to divulge sensitive information or take actions to help the adversary bypass the secure perimeter in front of the information-related resources so that the attacking goals can be completed. Though there are a number of security tools, such as firewalls and intrusion detection systems which are used to protect machines from being attacked, widely accepted mechanism to prevent dupe from fraud is lacking. However, the human element is often the weakest link of an information security chain, especially, in a human-centered environment. In this paper, we reveal that the human psychological weaknesses result in the main vulnerabilities that can be exploited by social engineering attacks. Also, we capture two essential levels, internal characteristics of human nature and external circumstance influences, to explore the root cause of the human weaknesses. We unveil that the internal characteristics of human nature can be converted into weaknesses by external circumstance influences. So, we propose the I-E based model of human weakness for social engineering investigation. Based on this model, we analyzed the vulnerabilities exploited by different techniques of social engineering, and also, we conclude several defense approaches to fix the human weaknesses. This work can help the security researchers to gain insights into social engineering from a different perspective, and in particular, enhance the current and future research on social engineering defense mechanisms.

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Synthesis of the Structure of a Computer System Functioning in Residual Classes

By Victor Krasnobayev Alexandr Kuznetsov Kateryna Kuznetsova

DOI: https://doi.org/10.5815/ijcnis.2023.01.01, Pub. Date: 8 Feb. 2023

An important task of designing complex computer systems is to ensure high reliability. Many authors investigate this problem and solve it in various ways. Most known methods are based on the use of natural or artificially introduced redundancy. This redundancy can be used passively and/or actively with (or without) restructuring of the computer system. This article explores new technologies for improving fault tolerance through the use of natural and artificially introduced redundancy of the applied number system. We consider a non-positional number system in residual classes and use the following properties: independence, equality, and small capacity of residues that define a non-positional code structure. This allows you to: parallelize arithmetic calculations at the level of decomposition of the remainders of numbers; implement spatial spacing of data elements with the possibility of their subsequent asynchronous independent processing; perform tabular execution of arithmetic operations of the base set and polynomial functions with single-cycle sampling of the result of a modular operation. Using specific examples, we present the calculation and comparative analysis of the reliability of computer systems. The conducted studies have shown that the use of non-positional code structures in the system of residual classes provides high reliability. In addition, with an increase in the bit grid of computing devices, the efficiency of using the system of residual classes increases. Our studies show that in order to increase reliability, it is advisable to reserve small nodes and blocks of a complex system, since the failure rate of individual elements is always less than the failure rate of the entire computer system.

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Protecting Hybrid Information Transmission Network from Natural and Anthropogenic Hazards

By Vadym Mukhin Pavlo Anakhov Viktoriia Zhebka Vladislav Kravchenko Aksieniia Shtimmerman Valerii Zavgorodnii Yurii Bazaka

DOI: https://doi.org/10.5815/ijcnis.2022.05.01, Pub. Date: 8 Oct. 2022

A hybrid network, which consists of the sections of communication lines with the transmission of signals of different physical nature on different transmission media, has been considered. Communication lines respond differently to threats, which allows to choose the line with the best performance for the transmission of information. The causal diagram of events that determine the state of the information transmission network, such as changes in emergency/accident-free time intervals, has been presented. The application scheme of the protection measures against dangerous events has been shown. To verify the measures, a matrix of their compliance with typical natural disasters has been developed and relevant examples have been given. It is suggested to evaluate the flexibility of the telecommunication network by its connectivity, characterized by the numbers of vertex and edge connectivity, the probability of connectivity. The presented scheme of the device for multi-channel information transmission in a hybrid network allows the choice for the transmission of information to the channel with the best performance. Using this device is the essence of the suggestion about increasing the flexibility of the network.

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Public vs Private vs Hybrid vs Community - Cloud Computing: A Critical Review

By Sumit Goyal

DOI: https://doi.org/10.5815/ijcnis.2014.03.03, Pub. Date: 8 Feb. 2014

These days cloud computing is booming like no other technology. Every organization whether it’s small, mid-sized or big, wants to adapt this cutting edge technology for its business. As cloud technology becomes immensely popular among these businesses, the question arises: Which cloud model to consider for your business? There are four types of cloud models available in the market: Public, Private, Hybrid and Community. This review paper answers the question, which model would be most beneficial for your business. All the four models are defined, discussed and compared with the benefits and pitfalls, thus giving you a clear idea, which model to adopt for your organization.

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Performance Analysis of 5G New Radio LDPC over Different Multipath Fading Channel Models

By Mohammed Hussein Ali Ghanim A. Al-Rubaye

DOI: https://doi.org/10.5815/ijcnis.2023.04.01, Pub. Date: 8 Aug. 2023

The creation and developing of a wireless network communication that is fast, secure, dependable, and cost-effective enough to suit the needs of the modern world is a difficult undertaking. Channel coding schemes must be chosen carefully to ensure timely and error-free data transfer in a noisy and fading channel. To ensure that the data received matches the data transmitted, channel coding is an essential part of the communication system's architecture. NR LDPC (New Radio Low Density Parity Check) code has been recommended for the fifth-generation (5G) to achieve the need for more internet traffic capacity in mobile communications and to provide both high coding gain and low energy consumption. This research presents NR-LDPC for data transmission over two different multipath fading channel models, such as Nakagami-m and Rayleigh in AWGN. The BER performance of the NR-LDPC code using two kinds of rate-compatible base graphs has been examined for the QAM-OFDM (Quadrature Amplitude Modulation-Orthogonal Frequency Division Multiplexing) system and compared to the uncoded QAM-OFDM system. The BER performance obtained via Monte Carlo simulation demonstrates that the LDPC works efficiently with two different kinds of channel models: those that do not fade and those that fade and achieves significant BER improvements with high coding gain. It makes sense to use LDPC codes in 5G because they are more efficient for long data transmissions, and the key to a good code is an effective decoding algorithm. The results demonstrated a coding gain improvement of up to 15 dB at 10-3 BER.

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Detecting Remote Access Network Attacks Using Supervised Machine Learning Methods

By Samuel Ndichu Sylvester McOyowo Henry Okoyo Cyrus Wekesa

DOI: https://doi.org/10.5815/ijcnis.2023.02.04, Pub. Date: 8 Apr. 2023

Remote access technologies encrypt data to enforce policies and ensure protection. Attackers leverage such techniques to launch carefully crafted evasion attacks introducing malware and other unwanted traffic to the internal network. Traditional security controls such as anti-virus software, firewall, and intrusion detection systems (IDS) decrypt network traffic and employ signature and heuristic-based approaches for malware inspection. In the past, machine learning (ML) approaches have been proposed for specific malware detection and traffic type characterization. However, decryption introduces computational overheads and dilutes the privacy goal of encryption. The ML approaches employ limited features and are not objectively developed for remote access security. This paper presents a novel ML-based approach to encrypted remote access attack detection using a weighted random forest (W-RF) algorithm. Key features are determined using feature importance scores. Class weighing is used to address the imbalanced data distribution problem common in remote access network traffic where attacks comprise only a small proportion of network traffic. Results obtained during the evaluation of the approach on benign virtual private network (VPN) and attack network traffic datasets that comprise verified normal hosts and common attacks in real-world network traffic are presented. With recall and precision of 100%, the approach demonstrates effective performance. The results for k-fold cross-validation and receiver operating characteristic (ROC) mean area under the curve (AUC) demonstrate that the approach effectively detects attacks in encrypted remote access network traffic, successfully averting attackers and network intrusions.

[...] Read more.
Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN

By Ramesh Vatambeti Vijay Kumar Damera Karthikeyan H. Manohar M. Sharon Roji Priya C. M. S. Mekala

DOI: https://doi.org/10.5815/ijcnis.2023.06.01, Pub. Date: 8 Dec. 2023

Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection.

[...] Read more.
D2D Communication Using Distributive Deep Learning with Coot Bird Optimization Algorithm

By Nethravathi H. M. Akhila S. Vinayakumar Ravi

DOI: https://doi.org/10.5815/ijcnis.2023.05.01, Pub. Date: 8 Oct. 2023

D2D (Device-to-device) communication has a major role in communication technology with resource and power allocation being a major attribute of the network. The existing method for D2D communication has several problems like slow convergence, low accuracy, etc. To overcome these, a D2D communication using distributed deep learning with a coot bird optimization algorithm has been proposed. In this work, D2D communication is combined with the Coot Bird Optimization algorithm to enhance the performance of distributed deep learning. Reducing the interference of eNB with the use of deep learning can achieve near-optimal throughput. Distributed deep learning trains the devices as a group and it works independently to reduce the training time of the devices. This model confirms the independent resource allocation with optimized power value and the least Bit Error Rate for D2D communication while sustaining the quality of services. The model is finally trained and tested successfully and is found to work for power allocation with an accuracy of 99.34%, giving the best fitness of 80%, the worst fitness value of 46%, mean value of 6.76 and 0.55 STD value showing better performance compared to the existing works.

[...] Read more.
Synthesis of the Structure of a Computer System Functioning in Residual Classes

By Victor Krasnobayev Alexandr Kuznetsov Kateryna Kuznetsova

DOI: https://doi.org/10.5815/ijcnis.2023.01.01, Pub. Date: 8 Feb. 2023

An important task of designing complex computer systems is to ensure high reliability. Many authors investigate this problem and solve it in various ways. Most known methods are based on the use of natural or artificially introduced redundancy. This redundancy can be used passively and/or actively with (or without) restructuring of the computer system. This article explores new technologies for improving fault tolerance through the use of natural and artificially introduced redundancy of the applied number system. We consider a non-positional number system in residual classes and use the following properties: independence, equality, and small capacity of residues that define a non-positional code structure. This allows you to: parallelize arithmetic calculations at the level of decomposition of the remainders of numbers; implement spatial spacing of data elements with the possibility of their subsequent asynchronous independent processing; perform tabular execution of arithmetic operations of the base set and polynomial functions with single-cycle sampling of the result of a modular operation. Using specific examples, we present the calculation and comparative analysis of the reliability of computer systems. The conducted studies have shown that the use of non-positional code structures in the system of residual classes provides high reliability. In addition, with an increase in the bit grid of computing devices, the efficiency of using the system of residual classes increases. Our studies show that in order to increase reliability, it is advisable to reserve small nodes and blocks of a complex system, since the failure rate of individual elements is always less than the failure rate of the entire computer system.

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A Critical appraisal on Password based Authentication

By Amanpreet A. Kaur Khurram K. Mustafa

DOI: https://doi.org/10.5815/ijcnis.2019.01.05, Pub. Date: 8 Jan. 2019

There is no doubt that, even after the development of many other authentication schemes, passwords remain one of the most popular means of authentication. A review in the field of password based authentication is addressed, by introducing and analyzing different schemes of authentication, respective advantages and disadvantages, and probable causes of the ‘very disconnect’ between user and password mechanisms. The evolution of passwords and how they have deep-rooted in our life is remarkable. This paper addresses the gap between the user and industry perspectives of password authentication, the state of art of password authentication and how the most investigated topic in password authentication changed over time. The author’s tries to distinguish password based authentication into two levels ‘User Centric Design Level’ and the ‘Machine Centric Protocol Level’ under one framework. The paper concludes with the special section covering the ways in which password based authentication system can be strengthened on the issues which are currently holding-in the password based authentication.

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Detecting Remote Access Network Attacks Using Supervised Machine Learning Methods

By Samuel Ndichu Sylvester McOyowo Henry Okoyo Cyrus Wekesa

DOI: https://doi.org/10.5815/ijcnis.2023.02.04, Pub. Date: 8 Apr. 2023

Remote access technologies encrypt data to enforce policies and ensure protection. Attackers leverage such techniques to launch carefully crafted evasion attacks introducing malware and other unwanted traffic to the internal network. Traditional security controls such as anti-virus software, firewall, and intrusion detection systems (IDS) decrypt network traffic and employ signature and heuristic-based approaches for malware inspection. In the past, machine learning (ML) approaches have been proposed for specific malware detection and traffic type characterization. However, decryption introduces computational overheads and dilutes the privacy goal of encryption. The ML approaches employ limited features and are not objectively developed for remote access security. This paper presents a novel ML-based approach to encrypted remote access attack detection using a weighted random forest (W-RF) algorithm. Key features are determined using feature importance scores. Class weighing is used to address the imbalanced data distribution problem common in remote access network traffic where attacks comprise only a small proportion of network traffic. Results obtained during the evaluation of the approach on benign virtual private network (VPN) and attack network traffic datasets that comprise verified normal hosts and common attacks in real-world network traffic are presented. With recall and precision of 100%, the approach demonstrates effective performance. The results for k-fold cross-validation and receiver operating characteristic (ROC) mean area under the curve (AUC) demonstrate that the approach effectively detects attacks in encrypted remote access network traffic, successfully averting attackers and network intrusions.

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Statistical Techniques for Detecting Cyberattacks on Computer Networks Based on an Analysis of Abnormal Traffic Behavior

By Zhengbing Hu Roman Odarchenko Sergiy Gnatyuk Maksym Zaliskyi Anastasia Chaplits Sergiy Bondar Vadim Borovik

DOI: https://doi.org/10.5815/ijcnis.2020.06.01, Pub. Date: 8 Dec. 2020

Represented paper is currently topical, because of year on year increasing quantity and diversity of attacks on computer networks that causes significant losses for companies. This work provides abilities of such problems solving as: existing methods of location of anomalies and current hazards at networks, statistical methods consideration, as effective methods of anomaly detection and experimental discovery of choosed method effectiveness. The method of network traffic capture and analysis during the network segment passive monitoring is considered in this work. Also, the processing way of numerous network traffic indexes for further network information safety level evaluation is proposed. Represented methods and concepts usage allows increasing of network segment reliability at the expense of operative network anomalies capturing, that could testify about possible hazards and such information is very useful for the network administrator. To get a proof of the method effectiveness, several network attacks, whose data is storing in specialised DARPA dataset, were chosen. Relevant parameters for every attack type were calculated. In such a way, start and termination time of the attack could be obtained by this method with insignificant error for some methods.

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Protecting Hybrid Information Transmission Network from Natural and Anthropogenic Hazards

By Vadym Mukhin Pavlo Anakhov Viktoriia Zhebka Vladislav Kravchenko Aksieniia Shtimmerman Valerii Zavgorodnii Yurii Bazaka

DOI: https://doi.org/10.5815/ijcnis.2022.05.01, Pub. Date: 8 Oct. 2022

A hybrid network, which consists of the sections of communication lines with the transmission of signals of different physical nature on different transmission media, has been considered. Communication lines respond differently to threats, which allows to choose the line with the best performance for the transmission of information. The causal diagram of events that determine the state of the information transmission network, such as changes in emergency/accident-free time intervals, has been presented. The application scheme of the protection measures against dangerous events has been shown. To verify the measures, a matrix of their compliance with typical natural disasters has been developed and relevant examples have been given. It is suggested to evaluate the flexibility of the telecommunication network by its connectivity, characterized by the numbers of vertex and edge connectivity, the probability of connectivity. The presented scheme of the device for multi-channel information transmission in a hybrid network allows the choice for the transmission of information to the channel with the best performance. Using this device is the essence of the suggestion about increasing the flexibility of the network.

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Ensemble Learning Approach for Classification of Network Intrusion Detection in IoT Environment

By Priya R. Maidamwar Prasad P. Lokulwar Kailash Kumar

DOI: https://doi.org/10.5815/ijcnis.2023.03.03, Pub. Date: 8 Jun. 2023

Over the last two years,the number of cyberattacks has grown significantly, paralleling the emergence of new attack types as intruder’s skill sets have improved. It is possible to attack other devices on a botnet and launch a man-in-the-middle attack with an IOT device that is present in the home network. As time passes, an ever-increasing number of devices are added to a network. Such devices will be destroyed completely if one or both of them are disconnected from a network. Detection of intrusions in a network becomes more difficult because of this. In most cases, manual detection and intervention is ineffective or impossible. Consequently, it's vital that numerous types of network threats can be better identified with less computational complexity and time spent on processing. Numerous studies have already taken place, and specific attacks are being examined. In order to quickly detect an attack, an IDS uses a well-trained classification model. In this study, multi-layer perceptron classifier along with random forest is used to examine the accuracy, precision, recall and f-score of IDS. IoT environment-based intrusion related benchmark datasets UNSWNB-15 and N_BaIoT are utilized in the experiment. Both of these datasets are relatively newer than other datasets, which represents the latest attack. Additionally, ensembles of different tree sizes and grid search algorithms are employed to determine the best classifier learning parameters. The research experiment's outcomes demonstrate the effectiveness of the IDS model using random forest over the multi-layer perceptron neural network model since it outperforms comparable ensembles analyzed in the literature in terms of K-fold cross validation techniques.

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Two-Layer Security of Images Using Elliptic Curve Cryptography with Discrete Wavelet Transform

By Ganavi M. Prabhudeva S.

DOI: https://doi.org/10.5815/ijcnis.2023.02.03, Pub. Date: 8 Apr. 2023

Information security is an important part of the current interactive world. It is very much essential for the end-user to preserve the confidentiality and integrity of their sensitive data. As such, information encoding is significant to defend against access from the non-authorized user. This paper is presented with an aim to build a system with a fusion of Cryptography and Steganography methods for scrambling the input image and embed into a carrier media by enhancing the security level. Elliptic Curve Cryptography (ECC) is helpful in achieving high security with a smaller key size. In this paper, ECC with modification is used to encrypt and decrypt the input image. Carrier media is transformed into frequency bands by utilizing Discrete Wavelet Transform (DWT). The encrypted hash of the input is hidden in high-frequency bands of carrier media by the process of Least-Significant-Bit (LSB). This approach is successful to achieve data confidentiality along with data integrity. Data integrity is verified by using SHA-256. Simulation outcomes of this method have been analyzed by measuring performance metrics. This method enhances the security of images obtained with 82.7528db of PSNR, 0.0012 of MSE, and SSIM as 1 compared to other existing scrambling methods.

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Predicting Intrusion in a Network Traffic Using Variance of Neighboring Object’s Distance

By Krishna Gopal Sharma Yashpal Singh

DOI: https://doi.org/10.5815/ijcnis.2023.02.06, Pub. Date: 8 Apr. 2023

Activities in network traffic can be broadly classified into two categories: normal and malicious. Malicious activities are harmful and their detection is necessary for security reasons. The intrusion detection process monitors network traffic to identify malicious activities in the system. Any algorithm that divides objects into two categories, such as good or bad, is a binary class predictor or binary classifier. In this paper, we utilized the Nearest Neighbor Distance Variance (NNDV) classifier for the prediction of intrusion. NNDV is a binary class predictor and uses the concept of variance on the distance between objects. We used KDD CUP 99 dataset to evaluate the NNDV and compared the predictive accuracy of NNDV with that of the KNN or K Nearest Neighbor classifier. KNN is an efficient general purpose classifier, but we only considered its binary aspect. The results are quite satisfactory to show that NNDV is comparable to KNN. Many times, the performance of NNDV is better than KNN. We experimented with normalized and unnormalized data for NNDV and found that the accuracy results are generally better for normalized data. We also compared the accuracy results of different cross validation techniques such as 2 fold, 5 fold, 10 fold, and leave one out on the NNDV for the KDD CUP 99 dataset. Cross validation results can be helpful in determining the parameters of the algorithm.

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