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With the wide application of IoT and industrial IoT technologies, the networkstructure is becoming more and more complex, and the traffic scale is growingrapidly, which makes the traditional security protection mechanism face seriouschallenges in dealing with high-frequency, diversified, and stealthycyber-attacks. To address this problem, this study proposes MindFlow, amulti-dimensional dynamic traffic prediction and anomaly detection systemcombining convolutional neural network (CNN) and bi-directional long andshort-term memory network (BiLSTM) architectures based on the MindSporeframework, and conducts systematic experiments on the NF-BoT-IoT dataset. Theexperimental results show that the proposed model achieves 99% in key metricssuch as accuracy, precision, recall and F1 score, effectively verifying itsaccuracy and robustness in network intrusion detection.

MindFlow: A Network Traffic Anomaly Detection Model Based on MindSpore