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计算机系统应用:2020,29(9):266-271
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基于长短期记忆网络的工控网络异常流量检测
(1.中国科学院大学, 北京 100049;2.中国科学院 沈阳计算技术研究所, 沈阳 110168;3.国网辽宁省电力有限公司, 沈阳 110004)
Detection of Abnormal Traffic in Industrial Control Network Based on LSTM Network
(1.University of Chinese Academy of Sciences, Beijing 100049, China;2.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China;3.State Grid Liaoning Electric Power Co. Ltd., Shenyang 110004, China)
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投稿时间:2020-02-20    修订日期:2020-03-17
中文摘要: 针对目前工控网络异常流量检测方法存在识别准确率不高和识别效率低的问题,结合工控网络具有周期性的特点,提出一种基于长短期记忆网络(LSTM)的时序预测的异常流量检测模型.该模型以LSTM网络模型为核心,用前15分钟的正常历史流量序列预测下一时刻的流量数据,在测试集上准确率为98.12%的前提下,可以认为模型的预测值即为正常值,通过对比实际值和预测值来判断是否出现异常.在不降低识别准确率的前提下,由于提前计算出了预测值,该方法大幅度提高了检测效率.
Abstract:Aiming at the problems of low recognition accuracy and low recognition efficiency in the current abnormal flow detection methods of industrial control network, combined with the periodic characteristics of industrial control networks, this study proposes an abnormal flow detection model based on Long-Short Term Memory network (LSTM) time series prediction. This model takes the LSTM network model as the core, and uses the normal historical traffic sequence of the first 15 minutes to predict the traffic data at the next moment. On the premise that the accuracy on the test set is 98.12%, the model’s predicted value can be considered to be normal. By comparing the actual value with the predicted value, it is determined whether there is an abnormality. On the premise of not reducing the recognition accuracy rate, because the predicted value is calculated in advance, this method greatly improves the detection efficiency.
文章编号:7598     中图分类号:    文献标志码:
基金项目:国家科技重大专项(2017ZX01030-201)
引用文本:
田伟宏,李喜旺,司志坚.基于长短期记忆网络的工控网络异常流量检测.计算机系统应用,2020,29(9):266-271
TIAN Wei-Hong,LI Xi-Wang,SI Zhi-Jian.Detection of Abnormal Traffic in Industrial Control Network Based on LSTM Network.COMPUTER SYSTEMS APPLICATIONS,2020,29(9):266-271

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