Juvenile 发表于 2025-3-26 22:55:58

Computational Collective Intelligence for security and privacy concerns and a new taxonomy proposed that could accommodate all potential threats. A detailed review of available security and privacy audit tools has also been done for common smart contract platforms. At last, identified the challenges required to be addressed to make the smart contract more efficient.

江湖骗子 发表于 2025-3-27 03:06:00

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粗野 发表于 2025-3-27 07:50:33

A Systematic Review of Challenges and Techniques of Privacy-Preserving Machine Learning,f privacy in ML, classifies current privacy threats, and describes state-of-the-art mitigation techniques named Privacy-Preserving Machine Learning (PPML) techniques. The paper compares existing PPML techniques based on relevant parameters, thereby presenting gaps in the existing literature and proposing probable future research drifts.

synovitis 发表于 2025-3-27 11:35:33

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变异 发表于 2025-3-27 16:54:11

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Esophagus 发表于 2025-3-27 20:21:34

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Chipmunk 发表于 2025-3-28 01:33:40

Smart Contract Security and Privacy Taxonomy, Tools, and Challenges, for security and privacy concerns and a new taxonomy proposed that could accommodate all potential threats. A detailed review of available security and privacy audit tools has also been done for common smart contract platforms. At last, identified the challenges required to be addressed to make the smart contract more efficient.

慢慢流出 发表于 2025-3-28 05:48:17

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dearth 发表于 2025-3-28 08:51:12

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思想 发表于 2025-3-28 12:13:37

Deep Learning Methods for Intrusion Detection System,his paper, an intrusion detection system is built using Deep Learning approaches Deep neural network and Convolutional Neural Network to detect DoS attacks. CICIDS2017 dataset is used to train the model and test the performance of the model. The experimental trials show that the proposed model outperforms all the previously implemented models.
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查看完整版本: Titlebook: Data Science and Security; Proceedings of IDSCS Samiksha Shukla,Aynur Unal,Dong Seog Han Conference proceedings 2021 The Editor(s) (if appl