An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications
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 def...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-03-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-024-00630-y |
| _version_ | 1797247015061553152 |
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| author | Uma Rani Sunil Kumar Neeraj Dahiya Kamna Solanki Shanu Rakesh Kuttan Sajid Shah Momina Shaheen Faizan Ahmad |
| author_facet | Uma Rani Sunil Kumar Neeraj Dahiya Kamna Solanki Shanu Rakesh Kuttan Sajid Shah Momina Shaheen Faizan Ahmad |
| author_sort | Uma Rani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T19:51:58Z |
| format | Article |
| id | doaj.art-ac15dcf946204a2b902fe231f2acd6dd |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-04-24T19:51:58Z |
| publishDate | 2024-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-ac15dcf946204a2b902fe231f2acd6dd2024-03-24T12:34:00ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2024-03-0113111910.1186/s13677-024-00630-yAn optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applicationsUma Rani0Sunil Kumar1Neeraj Dahiya2Kamna Solanki3Shanu Rakesh Kuttan4Sajid Shah5Momina Shaheen6Faizan Ahmad7Department of CSE, World College of Technology & ManagementDepartment of CSE, Guru Jambheshwar University of Science & TechnologyDepartment of CSE, SRM University Delhi-NCRDepartment of Computer Science Engineering, UIET, Maharshi Dayanand UniversityDepartment of CSE, Chouksey Engineering College BilaspurPrince Sultan UniversityDepartment of Computing, University of Roehampton LondonCardiff School of Technologies, Cardiff Metropolitan UniversityAbstract 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.https://doi.org/10.1186/s13677-024-00630-yMobile Edge Computing (MEC) Deep Neural network modelPost-training quantizationAdaHessian optimizerCryptojacking attackCrypto Exchange Operations |
| spellingShingle | Uma Rani Sunil Kumar Neeraj Dahiya Kamna Solanki Shanu Rakesh Kuttan Sajid Shah Momina Shaheen Faizan Ahmad An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications Journal of Cloud Computing: Advances, Systems and Applications Mobile Edge Computing (MEC) Deep Neural network model Post-training quantization AdaHessian optimizer Cryptojacking attack Crypto Exchange Operations |
| title | An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications |
| title_full | An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications |
| title_fullStr | An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications |
| title_full_unstemmed | An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications |
| title_short | An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications |
| title_sort | optimized neural network with adahessian for cryptojacking attack prediction for securing crypto exchange operations of mec applications |
| topic | Mobile Edge Computing (MEC) Deep Neural network model Post-training quantization AdaHessian optimizer Cryptojacking attack Crypto Exchange Operations |
| url | https://doi.org/10.1186/s13677-024-00630-y |
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