An FPGA-Based Performance Evaluation of Artificial Neural Network Architecture Algorithm for IoT

Teodoro, Arthur A. M.; Gomes, Otávio Souza Martins; Saadi, Muhammad; Silva, Bruno A.; Rosa, Renata Lopes; Rodríguez, Demóstenes Zegarra

doi:10.1007/s11277-021-08566-1

Cited by 30 publications

(14 citation statements)

References 51 publications

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“…The mean value is shown by the dashed lines that connect the graph's points, which may be used to compare disparities. The result in the range M = [25,30], ψ = 3 in Hebbian learning algorithms were addressed. The amount of iterations in the coordination process is strongly affected by the value of χ 1 − χ 2 − χ 3.…”

Section: Results and Analysismentioning

confidence: 99%

“…Huang [3], Jeong et al [6], and Teodoro et al [30] were first looked at in this study. Their shortcomings were also mentioned in this study.…”

Section: The Key Exchange Mechanisms Proposed By Dong Andmentioning

confidence: 94%

“…However, this method does not provide an effective syncing evaluation. To perform key exchange through reciprocal training of these systems, Teodoro et al [30] recommended to put TPM architecture on an FPGA.…”

Section: Related Workmentioning

confidence: 99%

“…This paper first examined the key exchange schemes proposed by Dong and Huang [3], Jeong et al [6], and Teodoro et al [30] quite recently. This paper also indicated their limitations.…”

Section: Conclusion and Future Scopementioning

confidence: 99%

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Chaos-guided neural key coordination for improving security of critical energy infrastructures

Sarkar

Khan

Noorwali

2021

Complex Intell. Syst.

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In this paper, chaos-guided artificial neural learning-based session key coordination for industrial internet-of-things (IIoT) to enhance the security of critical energy infrastructures (CEI) is proposed. An intruder might pose several security problems since the data are transferred across a public network. Although there have been substantial efforts to solve security problems in the IIoT, the majority of them have relied on traditional methods. A wide range of privacy issues (secrecy, authenticity, and access control) must be addressed to protect IIoT systems against attack. Owing to the unique characteristics of IIoT nodes, existing solutions do not properly address the entire security range of IIoT networks. To deal with this, a chaos-based triple layer vector-valued neural network (TLVVNN) is proposed in this paper. A chaos-based exchange of common seed value for the generation of the identical input vector at both transmitter and receiver is also proposed. This technique has several advantages, including (1) it protects IIoT devices by utilizing TLVVNN synchronization to improve CEI security. (2) Here, artificial neural coordination is utilized for the exchange of neural keys between two IIoT nodes. (3) Using this suggested methodology, chaotic synchronization can be achieved, enabling the chaos-based PRNG seed exchange. (4) Vector-valued inputs and weights are taken into consideration for TLVVNN networks. (5) The deep internal architecture is made up of three hidden layers of the neural network and a vector value as input. As a result, the attacker would have great difficulty interpreting the internal structure. Experiments to verify the performance of the proposed technique are conducted, and the findings demonstrate that the proposed technique has greater performance benefits than the existing related techniques.

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Section: Results and Analysismentioning

confidence: 99%

“…Huang [3], Jeong et al [6], and Teodoro et al [30] were first looked at in this study. Their shortcomings were also mentioned in this study.…”

Section: The Key Exchange Mechanisms Proposed By Dong Andmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

“…This paper first examined the key exchange schemes proposed by Dong and Huang [3], Jeong et al [6], and Teodoro et al [30] quite recently. This paper also indicated their limitations.…”

Section: Conclusion and Future Scopementioning

confidence: 99%

See 2 more Smart Citations

Chaos-guided neural key coordination for improving security of critical energy infrastructures

Sarkar

Khan

Noorwali

2021

Complex Intell. Syst.

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“…8. The key exchange strategies described by Jeong et al [12], Sarkar [31], Teodoro et al [34], Dong and Huang [8] and Dolecki and Kozera [7] were investigated in the present study. This research focused on their weaknesses as well.…”

Section: Introductionmentioning

confidence: 99%

Chaos coordinated neural key synchronization for enhancing security of IoT

Alahmadi

2022

Complex Intell. Syst.

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The key exchange mechanism in this paper is built utilizing neural network coordination and a hyperchaotic (or chaotic) nonlinear dynamic complex system. This approach is used to send and receive sensitive data between Internet-of-Things (IoT) nodes across a public network. Using phishing, Man-In-The-Middle (MITM), or spoofing attacks, an attacker can easily target sensitive information during the exchange process. Furthermore, minimal research has been made on the exchange of input seed values for creating identical input at both ends of neural networks. The proposed method uses a 5D hyperchaotic or chaotic nonlinear complex structure to ensure the sharing of input seed value across two neural networks, resulting in the identical input on both ends. This study discusses two ways for sharing seed values for neural coordination. The first is a chaotic system with all real variables, whereas the second is a hyperchaotic system with at least one complex variable. Each neural network has its own random weight vector, and the outputs are exchanged. It achieves full coordination in some stages by altering the neuronal weights according to the mutual learning law. The coordinated weights are utilized as a key after the neural coordination technique. The network’s core structure is made up of triple concealed layers. So, determining the inner configuration will be tough for the intruder. The efficiency of the suggested model is validated by simulations, and the findings reveal that the suggested strategy outperforms current equivalent techniques.

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Resource Allocation of a Modular ANN Implementation Using LabView and FPGA Board

Baccar

Hammi

Kallel

2023

Digital Technologies and Applications

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An FPGA-Based Performance Evaluation of Artificial Neural Network Architecture Algorithm for IoT

Cited by 30 publications

References 51 publications

Chaos-guided neural key coordination for improving security of critical energy infrastructures

Chaos-guided neural key coordination for improving security of critical energy infrastructures

Chaos coordinated neural key synchronization for enhancing security of IoT

Resource Allocation of a Modular ANN Implementation Using LabView and FPGA Board

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