文档介绍：: .
计算机工程与算法提取流量数据的时间序列特征，以检测更
加隐蔽的攻击。TCPDC 在 Electra 数据集上评估性能，实验结果证明了迁移学****在跨协议构建入侵检测
模型方面的可行性和有效性。
关键词：迁移学**** 入侵检测; 工业控制系统; 长短期记忆网络
文献标志码:A 中图分类号:; TP399 doi：.1002--0127
Research on Cross-Protocol Industrial Control Intrusion Detection System
FANG Guoqing, ZHANG Yaxian, YU Dan, MA Yao, CHEN Yongle
College of Information and Computer, Taiyuan University of Technology, Jinzhong, Shanxi 030600, China
Abstract：Industrial control systems are faced with severe security threats. Machine learning-based intrusion de-
tection technology relies on a large amount of labeled data. However, industrial control systems lack labeled data
and have many communication protocols. Data under different communication protocols are not universal. To solve
the above problems, a timing-sensitive cross-protocol domain confusion industrial control intrusion detection model
(TCPDC) is proposed. The model uses the domain confusion technique of transfer learning to minimize the distribu-
tion difference of traffic data under different communication protocols, transfer the knowledge learned under the old
communication protocol to the new communication protocol, and only use a small amount of unknown information
under the new communication protocol. By labeling the data, a high-accuracy intrusion detection model can be con-
structed. In addition, in order to achieve fine-grained identification of attack data, the model uses the Long
Short-Term Memory (LSTM) algorithm to extract the time-series features of traffic data to detect more stealthy at-
tacks. TCPDC evaluates the performance on the Electra dataset, and the experimental results demonstrate the feasi-
bility and effectiveness of transfer learn