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Wyświetlanie 1-4 z 4
Tytuł:
A cryptographic security mechanism for dynamic groups for public cloud environments
Autorzy:
Malviya, Sheenal
Dave, Sourabh
Bandhu, Kailash Chandra
Litoriya, Ratnesh
Powiązania:
https://bibliotekanauki.pl/articles/27314248.pdf
Data publikacji:
2022
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
cloud computing
cloud data security
dynamic groups
homomorphic encryption
chaotic network
throughput
avalanche effect
Opis:
Cloud computing has emerged as a significant technology domain, primarily due to the emergence of big data, machine learning, and quantum computing applications. While earlier, cloud computing services were focused mainly on providing storage and some infrastructures/ platforms for applications, the need to advance computational power analysis of massive datasets. It has made cloud computing almost inevitable from most client-based applications, mobile applications, or web applications. The allied challenge to protect data shared from and to cloud-based platforms has cropped up with the necessity to access public clouds. While conventional cryptographic algorithms have been used for securing and authenticating cloud data, advancements in cryptanalysis and access to faster computation have led to possible threats to the traditional security of cloud mechanisms. This has led to extensive research in homomorphic encryption pertaining to cloud security. In this paper, a security mechanism is designed targeted towards dynamic groups using public clouds. Cloud security mechanisms generally face a significant challenge in terms of overhead, throughput, and execution time to encrypt data from dynamic groups with frequent member addition and removal. A two-stage homomorphic encryption process is proposed for data security in this paper. The performance of the proposed system is evaluated in terms of the salient cryptographic metrics, which are the avalanche effect, throughput, and execution time. A comparative analysis with conventional cryptographic algorithms shows that the proposed system outperforms them regarding the cryptographic performance metrics.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 2; 46--54
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of multi-objective fruit fly optimisation algorithm based on population Manhattan distance in distribution network reconfiguration
Autorzy:
Tang, Minan
Zhang, Kaiyue
Wang, Qianqian
Cheng, Haipeng
Yang, Shangmei
Du, Hanxiao
Powiązania:
https://bibliotekanauki.pl/articles/1841286.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Chebyshev chaotic mapping
distributed generation
distribution network reconfiguration
fuzzy decision method
Pareto optimal
pmdMOFOA
population Manhattan distance
Opis:
In order to optimise the operation state of the distribution network in the presence of distributed generation (DG), to reduce network loss, balance load and improve power quality in the distribution system, a multi-objective fruit fly optimisation algorithm based on population Manhattan distance (pmdMOFOA) is presented. Firstly, the global and local exploration abilities of a fruit fly optimisation algorithm (FOA) are balanced by combining population Manhattan distance (PMD) and the dynamic step adjustment strategy to solve the problems of its weak local exploration ability and proneness to premature convergence. At the same time, Chebyshev chaotic mapping is introduced during position update of the fruit fly population to improve ability of fruit flies to escape the local optimum and avoid premature convergence. In addition, the external archive selection strategy is introduced to select the best individual in history to save in external archives according to the dominant relationship amongst individuals. The leader selection strategy, external archive update and maintenance strategy are proposed to generate a Pareto optimal solution set iteratively. Lastly, an optimal reconstruction scheme is determined by the fuzzy decision method. Compared with the standard FOA, the average convergence algebra of a pmdMOFOA is reduced by 44.58%. The distribution performance of non-dominated solutions of a pmdMOFOA, MOFOA, NSGA-III and MOPSO on the Pareto front is tested, and the results show that the pmdMOFOA has better diversity. Through the simulation and analysis of a typical IEEE 33-bus system with DG, load balance and voltage offset after reconfiguration are increased by 23.77% and 40.58%, respectively, and network loss is reduced by 57.22%, which verifies the effectiveness and efficiency of the proposed method.
Źródło:
Archives of Electrical Engineering; 2021, 70, 2; 307-323
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybrid evolutionary algorithm of optimized controller placement in SDN environment
Autorzy:
Hemagowri, J.
Tamil Selvan, P.
Powiązania:
https://bibliotekanauki.pl/articles/38704829.pdf
Data publikacji:
2023
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
controller
software defined network
Gaussian chaotic map
fish swarm
multi-criteria optimization
kontroler
sieć zdefiniowana programowo
mapa chaosu Gaussa
rój ryb
optymalizacja wielokryterialna
Opis:
Controller placement problem (CPP) is a significant technological challenge in software defined network (SDN). Deployment of a properly designed SDN-based network is required to detect optimal number of controllers for enhancing the network’s performance. However, the best possible controller placement for enhancing the network’s performance faces many issues. To solve the CPP, a novel technique called the hybrid evolutionary algorithm of optimized controller placement (HEA-OCP) in SDN environment is introduced to increase network’s performance by different network topologies. In the proposed model, optimized controller placement using improved multi-objective artificial fish optimization is employed to improve data transmission and reduce latency. Controller placement can be determined using an undirected graph based on a variety of factors, including propagation delay, load balancing capabilities and bandwidth, fault tolerance and data transfer rate, and a variety of other factors. For each controller, the fitness value is calculated over multi-criteria functions. The optimizer’s performance can be improved with the use of Gaussian chaotic maps. In large-scale SDN networks using HEC-OCP, the algorithm dynamically analyzes the optimal number of controllers and the best connections between switches and controllers. As a result, the overall network performance is improved and the delay minimization-based controller placement strategy is obtained. The simulation of HEA-OCP with existing methods is conducted by a network topology dataset of various metrics, namely packet delivery ratio, packet drop rate, throughput, average latency, and jitter. The proposed HEA-OCP improves the packet delivery and throughput with reduced average latency, and packet drop ensures more instantaneous communications in real-time applications of SDN for better decision-making.
Źródło:
Computer Assisted Methods in Engineering and Science; 2023, 30, 4; 539-556
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forecasting models for chaotic fractional-order oscillators using neural networks
Autorzy:
Bingi, Kishore
Prusty, B Rajanarayan
Powiązania:
https://bibliotekanauki.pl/articles/2055150.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
chaotic oscillators
data driven forecasting
fractional order system
model free analysis
neural network
time series prediction
oscylator chaotyczny
układ rzędu ułamkowego
sieć neuronowa
prognozowanie szeregów czasowych
Opis:
This paper proposes novel forecasting models for fractional-order chaotic oscillators, such as Duffing’s, Van der Pol’s, Tamaševičius’s and Chua’s, using feedforward neural networks. The models predict a change in the state values which bears a weighted relationship with the oscillator states. Such an arrangement is a suitable candidate model for out-of-sample forecasting of system states. The proposed neural network-assisted weighted model is applied to the above oscillators. The improved out-of-sample forecasting results of the proposed modeling strategy compared with the literature are comprehensively analyzed. The proposed models corresponding to the optimal weights result in the least mean square error (MSE) for all the system states. Further, the MSE for the proposed model is less in most of the oscillators compared with the one reported in the literature. The proposed prediction model’s out-of-sample forecasting plots show the best tracking ability to approximate future state values.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 3; 387--398
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-4 z 4

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