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

Uma Rani, Sunil Kumar, Neeraj Dahiya, Kamna Solanki, Shanu Rakesh Kuttan, Sajid Shah, Momina Shaheen, Faizan Ahmad
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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.
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利用 AdaHessian 优化神经网络预测加密劫持攻击,确保 MEC 应用程序的加密交换操作安全
由于 MEC 的广泛使用,比特币交易所的安全性至关重要。加密劫持破坏了 MEC 应用程序的安全性和比特币交易所生态系统的功能。本文提出了一种用于加密劫持预测和防御的前沿神经网络和 AdaHessian 优化技术。我们提供了一种前沿的深度神经网络(DNN)加密劫持攻击预测方法,该方法采用了剪枝、训练后量化和 AdaHessian 优化技术。为了解决这些问题,本文应用了剪枝、训练后量化和 AdaHessian 优化技术。利用 AdaHessian 优化的 DNN 快速训练新框架能以更低的计算成本检测出加密劫持企图。剪枝和训练后量化改进了低 CPU 边缘设备的模型。所提出的方法在不影响加密劫持攻击预测的情况下大幅降低了模型参数。该模型的 Recall 值为 98.72%,Precision 值为 98.91%,F1-Score 值为 99.09%,MSE 值为 0.0140,RMSE 值为 0.0137,MAE 值为 0.0139。我们的解决方案在精确度、计算效率和资源消耗方面都优于最先进的方法,从而可以建立更加真实、可信和经济高效的机器学习模型。我们通过完成 DNN 优化-安全循环,全面解决了日益严重的网络安全问题。Securing Crypto Exchange Operations 提供可扩展的高效加密劫持保护,改善机器学习、网络安全和网络管理。
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