Deep Learning-Based Community Detection Approach on Bitcoin Network

Essaid, Meryam; Ju, Hongteak

doi:10.3390/systems10060203

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…Recently, with the development of deep learning [2][3][4][5][6][7][8], various studies on deeplearning-based distinguishers [9,10] have been presented [11][12][13][14][15][16][17][18][19][20][21]. Deep learning is wellsuited for probabilistically distinguishing data that satisfy differential characteristics, as it has the capability to make probabilistic predictions about data.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Neural Distinguisher for Format-Preserving Encryption Schemes FF1 and FF3

Kim,

Jang

et al. 2024

Electronics

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Distinguishing data that satisfy the differential characteristic from random data is called a distinguisher attack. At CRYPTO’19, Gohr presented the first deep-learning-based distinguisher for round-reduced SPECK. Building upon Gohr’s work, various works have been conducted. Among many other works, we propose the first neural distinguisher using single and multiple differences for format-preserving encryption (FPE) schemes FF1 and FF3. We harnessed the differential characteristics used in FF1 and FF3 classical distinguishers. They used SKINNY as the inner encryption algorithm for FF3. On the other hand, we employ the standard FF1 and FF3 implementations with AES encryption (which may be more robust). This work utilizes the differentials employed in FF1 and FF3 classical distinguishers. In short, when using a single 0x0F (resp. 0x08) differential, we achieve the highest accuracy of 0.85 (resp. 0.98) for FF1 (resp. FF3) in the 10-round (resp. 8-round) number domain. In the lowercase domain, due to an increased number of plaintext and ciphertext combinations, we can distinguish with the highest accuracy of 0.52 (resp. 0.55) for FF1 (resp. FF3) in a maximum of 2 rounds. Furthermore, we present an advanced neural distinguisher designed with multiple differentials for FF1 and FF3. With this sophisticated model, we still demonstrate valid accuracy in guessing the input difference used for encryption.

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Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Neural Distinguisher for Format-Preserving Encryption Schemes FF1 and FF3

Kim,

Jang

et al. 2024

Electronics

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“…The findings demonstrated how well the suggested deep learning with hybrid optimisation works for identifying communities in large networks. Essaid et al [16] presented a method for detecting communities inside the Bitcoin network that uses a deep feature representation algorithm and Deep Feedforward Autoencoders. Their findings demonstrated that, compared to a random P2P network, the Bitcoin network has a stronger clustering coefficient and community structure.…”

Section: Introductionmentioning

confidence: 99%

A Deep Community Detection Approach in Real Time Networks

Choudhury,

Acharjee

2023

SCPE

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Community detection in real time networks is one of the important aspect of social network analysis. Deep learning has been applied successfully in a variety of research fields in recent years. Proximity matrix is frequently used as the representation of the network structure. However, there are issues with the proximity matrix's insufficient spatial contiguity information. As a result, this research provides a deep learning applied community identification approach that combines the reorganization of the matrices, spatial attribute uprooting, and community identification. For obtaining a spatial proximity matrix, the primary proximity matrices in a real time graph is recreated using the highest weight and adjacent users. The dimensional proximity matrix can obtain a subdomain of the network, allowing the convolutional neural network (CNN) to draw out dimensional localization more easily and fast. Ten different real time datasets of social networks are used in tests to examine our proposed approach. Our results show that the proposed community identification approach has higher compatibility than existing deep learning-based strategies. As a result, the proposed deep community identification approach is capable of detecting the excellent clusters in real time networks.

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Deep Learning-Based Community Detection Approach on Bitcoin Network

Cited by 2 publications

References 27 publications

Deep-Learning-Based Neural Distinguisher for Format-Preserving Encryption Schemes FF1 and FF3

Deep-Learning-Based Neural Distinguisher for Format-Preserving Encryption Schemes FF1 and FF3

A Deep Community Detection Approach in Real Time Networks

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