The rapid growth of online music education has led to increased security risks from cyber intrusions. This paper proposes public cloud networks oriented deep neural networks for effective intrusion detection in online music education environments. Specifically, a novel intrusion detection framework is developed, comprising fuzzy logic based feature selection, chronological salp swarm algorithm optimized deep belief networks, and gated recurrent unit integrated convolutional neural networks. Detailed methodologies are presented for the fuzzy logic system, chronological salp optimization, deep belief network architecture, and convolutional neural networks. Comprehensive experiments are conducted on the NSL-KDD and CICIDS2017 datasets. Various deep neural networks are evaluated and compared, including multi-layer perceptrons, convolutional neural networks, deep belief networks, recurrent neural networks, and the proposed models. Experimental results demonstrate that the proposed fuzzy feature selection and chronological salp swarm algorithm optimized deep belief network achieves a test accuracy of 97.33 %, outperforming other peer models. The gated recurrent unit integrated convolutional neural network obtains a test accuracy of 98.46 %, superior to state-of-the-art methods. While the experiments on the newly created dataset for intrusion detection in cloud-based online music education demonstrate that the proposed models outperform the benchmarks. The experiments verify the effectiveness of the proposed deep learning frameworks for intrusion detection in online music education cloud networks.

中文翻译：

---

面向公共云网络的深度神经网络，用于在线音乐教育中的有效入侵检测

在线音乐教育的快速增长导致网络入侵的安全风险增加。本文提出了面向公共云网络的深度神经网络，用于在线音乐教育环境中的有效入侵检测。具体来说，开发了一种新颖的入侵检测框架，包括基于模糊逻辑的特征选择、按时间顺序排列的樽海鞘算法优化的深度置信网络和门控循环单元集成卷积神经网络。提出了模糊逻辑系统、按时间顺序排列的樽海鞘优化、深度置信网络架构和卷积神经网络的详细方法。在NSL-KDD和CICIDS2017数据集上进行了全面的实验。评估和比较了各种深度神经网络，包括多层感知器、卷积神经网络、深度信念网络、循环神经网络和所提出的模型。实验结果表明，所提出的模糊特征选择和时间顺序樽海鞘算法优化的深度置信网络的测试准确率达到了97.33%，优于其他同行模型。门控循环单元集成卷积神经网络的测试准确率达到 98.46%，优于最先进的方法。而在基于云的在线音乐教育中用于入侵检测的新创建数据集上的实验表明，所提出的模型优于基准。实验验证了所提出的在线音乐教育云网络入侵检测深度学习框架的有效性。