用CNN-BiLSTM模型结合MindSpore框架，实现高精度网络异常检测。
Research on CNN-BiLSTM Network Traffic Anomaly Detection Model Based on MindSpore
- 融合CNN与BiLSTM，捕捉流量时序与局部特征。
- 在NF-BoT-IoT数据集上达到99%的准确率、精确率、召回率和F1分数。
- 适合物联网安全防护场景，尤其适用于复杂网络环境下的入侵检测。
随着物联网（IoT）与工业物联网（IIoT）技术的广泛应用，网络架构日益复杂，流量规模急剧增长。这一演变给传统安全机制带来巨大挑战，尤其是在检测高频、多样且隐蔽性强的网络攻击方面。为应对这些挑战，本文提出一种新型网络流量异常检测模型，该模型将卷积神经网络（CNN）与双向长短期记忆网络（BiLSTM）相结合，并基于MindSpore框架实现。在NF-BoT-IoT数据集上进行的全面实验表明，所提模型在准确率、精确率、召回率和F1分数上均达到99%，展现出优异的性能与鲁棒性，适用于网络入侵检测任务。
原文摘要 · Abstract (English)
With the widespread adoption of the Internet of Things (IoT) and Industrial IoT (IIoT) technologies, network architectures have become increasingly complex, and the volume of traffic has grown substantially. This evolution poses significant challenges to traditional security mechanisms, particularly in detecting high-frequency, diverse, and highly covert network attacks. To address these challenges, this study proposes a novel network traffic anomaly detection model that integrates a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network, implemented on the MindSpore framework. Comprehensive experiments were conducted using the NF-BoT-IoT dataset. The results demonstrate that the proposed model achieves 99% across accuracy, precision, recall, and F1-score, indicating its strong performance and robustness in network intrusion detection tasks.
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