TY - GEN
T1 - Network data analysis and anomaly detection using CNN technique for industrial control systems security
AU - Hu, Yibo
AU - Zhang, DInghua
AU - Cao, Guoyan
AU - Pan, Quan
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Industrial control system (ICS) security is an important topic in context of Industry 4.0. Due to limited industrial network data and insufficient data analysis, anomaly detection for ICS is hardly to implement to enforce ICS security. The paper analyzes the traditional IT network data and ICS network data, bridges the common knowledge of network flow data feature of the both networks, and transfers the anomaly detection knowledge of traditional IT network into ICS network by means of a designed convolutional neural network (CNN) mechanism. Specific experiments validate the accuracy and reliability of the proposed CNN mechanism.
AB - Industrial control system (ICS) security is an important topic in context of Industry 4.0. Due to limited industrial network data and insufficient data analysis, anomaly detection for ICS is hardly to implement to enforce ICS security. The paper analyzes the traditional IT network data and ICS network data, bridges the common knowledge of network flow data feature of the both networks, and transfers the anomaly detection knowledge of traditional IT network into ICS network by means of a designed convolutional neural network (CNN) mechanism. Specific experiments validate the accuracy and reliability of the proposed CNN mechanism.
UR - http://www.scopus.com/inward/record.url?scp=85076725234&partnerID=8YFLogxK
U2 - 10.1109/SMC.2019.8913895
DO - 10.1109/SMC.2019.8913895
M3 - 会议稿件
AN - SCOPUS:85076725234
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 593
EP - 597
BT - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Y2 - 6 October 2019 through 9 October 2019
ER -