為了解決具有相同訊息的來源身份驗證問題,本研究提出了一種混合架構,
其中通過使用深度學習神經網路發現數據集中的重複項。而區塊鏈部分為訊息
源提供了不同的元數據,使發送者和接收者都能夠意識到訊息是準確的,不能
被修改或更改。
此外,該系統還部署了基於RNN 的LSTM 和GRU 模型神經網路,以確保
該方法在本系統應用中是否表現更好,並利用以太坊區塊鏈在雙方之間進行交
易。此外,我們的系統還使用了高級加密標準(AES)技術來保護它們。在這
裡AES技術使用加密和解密密鑰方式。這個系統結構的想法已經在樹莓派設備
上執行並在小系統流程圖中研究了它的可行性。
最後,本研究使用的數據集是來自Kaggle 網站的“圖像加密和解密數據
集”,性能結果表明,與GRU 相比,LSTM 算法在特定數據集中的準確率達
到0.9635。智慧合約模組具有可驗證性、可用性、防篡改和可行性4 個屬性,
而AES 為系統提供了更強大的加密方法。

To address the problem of source authentication and duplicated information,
this study proposes a hybrid architecture in which duplicates in the dataset are

found using a neural network. While the blockchain part provides a distinct in-
formation source, enabling both the sender and the recipient to know that the

information is accurate and cannot be modified or altered. I have deployed RNN-
based LSTM and GRU models in the neural networks to determine whether the

method performs better in these applications. I have also utilized the Ethereum

blockchain to carry out transactions between the two parties and used the Ad-
vanced Encryption Standard(AES) technique to protect them. Here, encryption

and decryption keys are on the AES technique. I have used Raspberry Pi to
implement it to look into its feasibility in small systems.
The dataset used in this paper is the “Image encryption and decryption dataset”
by Kaggle and the performance results show that the LSTM algorithm performs
well compared to GRU yielding a 0.9635 accuracy rate on the particular dataset.
Our smart contract module has 4 properties which are verifiability, availability,
tamper-resistance, and usability while AES provides a stronger encryption method
for the system.

Abstract (Chinese) I
Abstract II
Acknowledgements III
Contents IV
List of Figures VI
List of Tables VIII
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4.1 Ethereum Blockchain . . . . . . . . . . . . . . . . . . . . . 7
1.4.2 Transaction and Gas . . . . . . . . . . . . . . . . . . . . . . 8
1.4.3 Smart Contract . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4.4 Difference Between Contract and Traditional Smart Contract 8
1.4.5 Accounts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.5 Recurrent Neural Network . . . . . . . . . . . . . . . . . . . . . . . 9
IV
1.6 Decentralized Apps . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 Related Works 12
3 Proposed Methodology 18
3.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2 Data Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3 AES Encryption: . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.4 Smart Contract for Light Devices . . . . . . . . . . . . . . . . . . . 25
3.5 Deep learning - GRU and LSTM Algorithm . . . . . . . . . . . . . 28
3.6 Gated Recurrent Unit- GRU algorithm . . . . . . . . . . . . . . . . 29
3.7 LSTM ( Long Short Term Memory Algorithm) . . . . . . . . . . . . 31
4 Evaluation Results and Discussion 35
4.1 Performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2 Comparative analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5 Conclusion 40
6 Discussion and Future Work 42
Bibliography 44

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