Network intrusion detection using fusion features and convolutional bidirectional recurrent neural network

Jagruthi, H; Kavitha, C.; Mulimani, Manjunath

doi:10.1504/ijcat.2022.126095

Cited by 2 publications

(1 citation statement)

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“…The loss function is set, the gradient is calculated, and the model is updated along the opposite direction. When solving the parameters will be performed in the DPAGD-CNN model, it is necessary to set the loss function that should be set, then calculate the gradient of the model using the training samples, and finally make the model change in the opposite direction of the descending gradient [23][24][25] . Fig.…”

Section: Construction Of Dpagd-cnn Privacy Protection Model On the Gr...mentioning

confidence: 99%

Privacy Protection Based on Federated Learning

Liu,

Eric B. Blancaflor,

Fang

et al. 2024

JAIT

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With the development of artificial intelligence technology, more and more fields will collect relevant user data, and provide users with a better experience through data analysis. But there are also risks involved in the process of data collection, namely how to protect personal privacy data. To address this issue, this study combined differential privacy, convolutional neural networks, and federated averaging algorithms to construct a privacy protection model. The study first utilized the federated average algorithm to handle data imbalance, ensuring that each analyzed data is in a balanced state. Then, based on of data balancing, a new algorithm model was constructed using differential privacy and convolutional neural networks. Finally, it utilized a number of public datasets to verify the role of the model in privacy protection. The results showed that the model can achieve recognition accuracy of 97.27% and 93.15%, respectively , for data under the influence of privacy budget and relaxation factor. Meanwhile, the classification accuracy of the model for data can reached 95.31%, with a regression error of 9.03%. When the local iteration number of the device was 30, the testing accuracy can reached 95.28%. This indicates that methods on the grounds of federated averaging algorithm and differential privacy can maintain the accuracy of the model while protecting user privacy. The application research of models has strong practical significance.

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Section: Construction Of Dpagd-cnn Privacy Protection Model On the Gr...mentioning

confidence: 99%