信息与控制2018，Vol.47Issue(1)：54-59,6.DOI:10.13976/j.cnki.xk.2018.0054
基于长短时记忆网络的工业控制系统入侵检测
Intrusion Detection of Industrial Control System Based on Long Short Term Memory
摘要
Abstract
We propose an industrial control system intrusion detection method based on long short term memory ( LSTM) networks to handle massive, high-dimensional network traffic data samples in the industrial control system ( ICS) . Firstly, we employed the synthetic minority oversampling technique since the original data set has imbalanced samples. Then, we optimized the LSTM model the cross-validation method. Finally, a com-parison experiment with the traditional intrusion detection method is investigated using the standard industrial data set. The results show that the LSTM-based intrusion detection method had a higher accuracy than the tra-ditional method after data preprocessing.关键词
工业控制系统/入侵检测/不平衡数据/深度学习/长短时记忆网络Key words
industrial control system/intrusion detection/imbalanced data/deep learning/long short term memory分类
信息技术与安全科学引用本文复制引用
於帮兵,王华忠,颜秉勇..基于长短时记忆网络的工业控制系统入侵检测[J].信息与控制,2018,47(1):54-59,6.基金项目
国家自然科学基金青年基金资助项目(51407078) （51407078）
