Abstract
In view of the complex hospital network environment, massive redundancy of security alarms, and lag in identification of real threats, this study proposes a hospital network security alarm model based on optimized Long Short-Term Memory (LSTM)-Informer. First, an attention-enhanced multi-dimensional alarm feature fusion module is constructed to integrate device logs, traffic data, and threat intelligence unique to hospital networks. Second, a hybrid bidirectional LSTM (BiLSTM) and improved Informer structure is designed to enhance long-range correlation mining and short-term burst alarm detection. The sparse attention mechanism is optimized with hospital threat-level priors to improve detection accuracy and reduce false alarms. The results show that among long-term correlation alarm types, the recall rate of port scanning, intranet lateral movement, and ransomware behavior are all ≥94.5%, and the false alarm rate is ≤2.8%, proving that the model effectively filters redundant alarms in long-term sequences. Among the short-term burst alarm types, the F1 score of malicious traffic injection and data leakage attempts is ≥93.8%. Since the characteristics of phishing emails are more subtle, the false positive rate is slightly higher (4.0%), but it is still far lower than the traditional LSTM model (10.8%). The performance of the optimized model is significantly improved compared with the traditional model, and it can effectively alleviate the alarm fatigue of security operation personnel. This study provides an efficient alarm research and judgment solution for hospital network security intelligent operation and maintenance, helping to achieve active defense.
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