Packet filtering by artificial neural network

Turčaník, Michal

doi:10.1109/miltechs.2015.7153739

Cited by 22 publications

(5 citation statements)

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“…This section highlights discusses, compares, summarizes and critiques more than eighty research articles on artificial neural network model's application to the diverse area of the economy. The comparison was made based on (i) author(s)/year of publication (ii) ANN modeling (iii) ANN area of application (iii) studied contribution to human challenges (with references to Supplementary Table 2) [ 11 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , …”

Section: Main Textmentioning

confidence: 99%

State-of-the-art in artificial neural network applications: A survey

Abiodun

Omolara

Dada

et al. 2018

Heliyon

2,160

785

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This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.

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Section: Main Textmentioning

confidence: 99%

State-of-the-art in artificial neural network applications: A survey

Abiodun

Omolara

Dada

et al. 2018

Heliyon

2,160

785

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show abstract

“…In this paper, a resource-efficient optimal routing scheme is devised for CR-IoT is proposed, where the controllers in CR-IoT aims to support the CR-IoT network architecture using ML optimization to obtain an optimal routing. The employability of routing capability [21][22][23][24] using artificial neural networks [25][26][27] enables CR-IoT to select optimal paths for actual data transmission. The ANN routing addresses various constraints of CR-IoT in a dynamic way, where the resources are allocated without prior information in relation with the network dynamics.…”

Section: Introductionmentioning

confidence: 99%

An IoT and machine learning‐based routing protocol for reconfigurable engineering application

Natarajan¹,

Srihari

Chandragandhi³

et al. 2021

IET Communications

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With new telecommunications engineering applications, the cognitive radio (CR) networkbased internet of things (IoT) resolves the bandwidth problem and spectrum problem. However, the CR-IoT routing method sometimes presents issues in terms of road finding, spectrum resource diversity and mobility. This study presents an upgradable cross-layer routing protocol based on CR-IoT to improve routing efficiency and optimize data transmission in a reconfigurable network. In this context, the system is developing a distributed controller which is designed with multiple activities, including load balancing, neighbourhood sensing and machine-learning path construction. The proposed approach is based on network traffic and load and various other network metrics including energy efficiency, network capacity and interference, on an average of 2 bps/Hz/W. The trials are carried out with conventional models, demonstrating the residual energy and resource scalability and robustness of the reconfigurable CR-IoT. INTRODUCTIONWireless networks reconfigurable (RWN) is mainly an adaptive network firmware developed to satisfy the demands of modern applications, changing network topologies and changing network conditions. In particular, the RWM can be reconfiguredThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…An efficient optimising routing technique is provided in CRN. The use of artificial neural networks [25][26][27] routing capability [25,26,[28][29][30][31][32][33][34][35] allows CRN to identify the right paths for the actual transfer of information. The ANN routing addresses certain dynamic limitations of the CRN, where resources are distributed in conjunction with the network dynamics without prior information.…”

Section: Introductionmentioning

confidence: 99%

SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer

Dhiman¹,

Sharma

2021

Complex Intell. Syst.

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In the case of new technology application, the cognitive radio network (CRN) addresses the bandwidth shortfall and the fixed spectrum problem. The method for CRN routing, however, often encounters issues with regard to road discovery, diversity of resources and mobility. In this paper, we present a reconfigurable CRN-based cross-layer routing protocol with the purpose of increasing routing performance and optimizing data transfer in reconfigurable networks. Recently developed spotted hyena optimizer (SHO) is used for tuning the hyperparameters of machine-learning models. The system produces a distributor built with a number of tasks, such as load balance, quarter sensing and the development path of machine learning. The proposed technique is sensitive to traffic and charges, as well as a series of other network metrics and interference (2bps/Hz/W average). The tests are performed with classic models that demonstrate the residual energy and strength of the resistant scalability and resource.

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Packet filtering by artificial neural network

Cited by 22 publications

References 1 publication

State-of-the-art in artificial neural network applications: A survey

State-of-the-art in artificial neural network applications: A survey

An IoT and machine learning‐based routing protocol for reconfigurable engineering application

SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer

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