| 融合注意力机制和BSRU的工业互联网安全态势预测方法 |
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| 作者姓名： | 胡向东 田正国 |
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| 作者单位： | 重庆邮电大学自动化学院/工业互联网学院，重庆 400065 |
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| 基金项目： | 教育部-中国移动科研基金(MCM20150202);教育部-中国移动科研基金(MCM20180404) |
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| 摘 要： | 安全态势预测对确保工业互联网平稳可靠运行至关重要.传统的预测模型在面对工业生产过程中产生的海量、高维和时间序列数据时,难以准确、高效地对网络安全态势进行预测,因此提出一种融合注意力机制和双向简单循环单元(BSRU,bi-directional simple recurrent unit)的工业互联网安全态势预测方法,以...
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| 关 键 词： | 工业互联网 注意力机制 简单循环单元 安全态势 |
Methods of security situation prediction for industrial internet fused attention mechanism and BSRU |
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| Authors: | Xiangdong HU Zhengguo TIAN |
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| Affiliation: | College of Automation/Industrial Internet Institute, Chongqing University of Posts and Telecommunications, Chongqing 400065, China |
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| Abstract: | The security situation prediction plays an important role in balanced and reliable work for industrial internet.In the face of massive, high-dimensional and time-series data generated in the industrial production process, traditional prediction models are difficult to accurately and efficiently predict the network security situation.Therefore, the methods of security situation prediction for industrial internet fused attention mechanism and bi-directional simple recurrent unit (BSRU) were proposed to meet the real-time and accuracy requirements of industrial production.Each security element was analyzed and processed, so that it could reflect the current network state and facilitate the calculation of the situation value.One-dimensional convolutional network was used to extract the spatial dimension features between each security element and preserve the temporal correlation between features.The BSRU network was used to extract the time dimension features between the data information and reduced the loss of historical information.Meanwhile, with the powerful parallel capability of SRU network, the training time of model was reduced.Attention mechanism was introduced to optimize the correlation weight of BSRU hidden state to highlight strong correlation factors, reduced the influence of weak correlation factors, and realized the prediction of industrial internet security situation combining attention mechanism and BSRU.The comparative experimental results show that the model reduces the training time and training error by 13.1% and 28.5% than the model using bidirectional long short-term memory network and bidirectional gated recurrent unit.Compared with the convolutional and BSRU network fusion model without attention mechanism, the prediction error is reduced by 28.8% despite the training time increased by 2%.The prediction effect under different prediction time is better than other models.Compared with other prediction network models, this model achieves the optimization of time performance and uses the attention mechanism to improve the prediction accuracy of the model under the premise of increasing a small amount of time cost.The proposed model can well fit the trend of network security situation, meanwhile, it has some advantages in multistep prediction. |
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| Keywords: | industrial internet attention mechanism simple recurrent unit security situation |
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