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An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications.
- Source :
- Journal of Cloud Computing (2192-113X); 3/18/2024, Vol. 13 Issue 1, p1-19, 19p
- Publication Year :
- 2024
-
Abstract
- Bitcoin exchange security is crucial because of MEC's widespread use. Cryptojacking has compromised MEC app security and bitcoin exchange ecosystem functionality. This paper propose a cutting-edge neural network and AdaHessian optimization technique for cryptojacking prediction and defense. We provide a cutting-edge deep neural network (DNN) cryptojacking attack prediction approach employing pruning, post-training quantization, and AdaHessian optimization. To solve these problems, this paper apply pruning, post-training quantization, and AdaHessian optimization. A new framework for quick DNN training utilizing AdaHessian optimization can detect cryptojacking attempts with reduced computational cost. Pruning and post-training quantization improve the model for low-CPU on-edge devices. The proposed approach drastically decreases model parameters without affecting Cryptojacking attack prediction. The model has Recall 98.72%, Precision 98.91%, F1-Score 99.09%, MSE 0.0140, RMSE 0.0137, and MAE 0.0139. Our solution beats state-of-the-art approaches in precision, computational efficiency, and resource consumption, allowing more realistic, trustworthy, and cost-effective machine learning models. We address increasing cybersecurity issues holistically by completing the DNN optimization-security loop. Securing Crypto Exchange Operations delivers scalable and efficient Cryptojacking protection, improving machine learning, cybersecurity, and network management. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2192113X
- Volume :
- 13
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Journal of Cloud Computing (2192-113X)
- Publication Type :
- Academic Journal
- Accession number :
- 176120001
- Full Text :
- https://doi.org/10.1186/s13677-024-00630-y