التفاصيل البيبلوغرافية
| العنوان: |
基于TCN-BiLSTM的網絡安全態勢預測. (Chinese) |
| Alternate Title: |
Network security situation prediction based on TCN-BiLSTM. (English) |
| المؤلفون: |
孙隽丰, 李成海, 曹 波 |
| المصدر: |
Systems Engineering & Electronics; Nov2023, Vol. 45 Issue 11, p3671-3679, 9p |
| مصطلحات موضوعية: |
PARTICLE swarm optimization, COMPUTER network security, TIME-varying networks, FORECASTING |
| Abstract (English): |
In order to solve the problems of low prediction accuracy and slow convergence speed of existing network security situation prediction models, a prediction method based on temporal convolution network (TCN) and bi-directional long short-term memory (BiLSTM) network is proposed. This method firstly applies the advantages of TCN in dealing with time series problems to the sequence characteristics of learning potential values in situation prediction, then introduces the attention mechanism to dynamically adjust the weights of attributes. Secondly, the proposed method uses the status before and after learning potential values of BiLSTM model to extract more information from the series for prediction. Particle swarm optimization (PSO) algorithm is used to optimize the hyperparameters to improve the prediction ability. The experimental results show that the fitting degree of the proposed prediction method can reach 0.9995, and its fitting effect and convergence speed are better than other models. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
針對現有網絡安全態勢預測模型預測精確度低和收斂速度慢的問題，提出一種基于時域卷積網絡（temporal convolution network, TCN）和雙向長短期記憶（bi-directional long short-term memory, BiLSTM）網絡的預測方法。首先，將TCN處理時間序列問題的優勢應用到態勢預測上學習態勢值的序列特征；隨后，引入注意力機制動態調整屬性的權值；然后，利用BiLSTM模型學習態勢值的前后狀況，以提取序列中更多的信息進行預測；利用粒子群優化（particle swarm optimization, PSO）算法進行超參數尋優，提升預測能力。實驗結果表明，所提預測方法的擬合度可達0.999 5,其擬合效果和收斂速度均優于其他模型。 [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
