The Devil is in the Details: Unwrapping the Cryptojacking Malware Ecosystem on Android

Adjibi, Boladji Vinny; Mbodji, Fatou Ndiaye; Bissyandé, Tegawendé F.; Allix, Kevin; Klein, Jacques

doi:10.1109/scam55253.2022.00023

2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM) 2022

DOI: 10.1109/scam55253.2022.00023

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The Devil is in the Details: Unwrapping the Cryptojacking Malware Ecosystem on Android

Boladji Vinny Adjibi

Fatou Ndiaye Mbodji

Tegawendé F. Bissyandé

et al.

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2024

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An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications

Rani,

Kumar,

Dahiya

et al. 2024

J Cloud Comp

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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.

show abstract

An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications

Rani,

Kumar,

Dahiya

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

show abstract

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The Devil is in the Details: Unwrapping the Cryptojacking Malware Ecosystem on Android

Cited by 1 publication

References 28 publications

An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications

An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications

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