TY - GEN
T1 - Network Security Situation Prediction in Software Defined Networking Data Plane
AU - Sheng, Mingren
AU - Liu, Hongri
AU - Yang, Xu
AU - Wang, Wei
AU - Huang, Junheng
AU - Wang, Bailing
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - Software-Defined Networking (SDN) simplifies network management by separating the control plane from the data forwarding plane. However, the plane separation technology introduces many new loopholes in the SDN data plane. In order to facilitate taking proactive measures to reduce the damage degree of network security events, this paper proposes a security situation prediction method based on particle swarm optimization algorithm and long-short-term memory neural network for network security events on the SDN data plane. According to the statistical information of the security incident, the analytic hierarchy process is used to calculate the SDN data plane security situation risk value. Then use the historical data of the security situation risk value to build an artificial neural network prediction model. Finally, a prediction model is used to predict the future security situation risk value. Experiments show that this method has good prediction accuracy and stability.
AB - Software-Defined Networking (SDN) simplifies network management by separating the control plane from the data forwarding plane. However, the plane separation technology introduces many new loopholes in the SDN data plane. In order to facilitate taking proactive measures to reduce the damage degree of network security events, this paper proposes a security situation prediction method based on particle swarm optimization algorithm and long-short-term memory neural network for network security events on the SDN data plane. According to the statistical information of the security incident, the analytic hierarchy process is used to calculate the SDN data plane security situation risk value. Then use the historical data of the security situation risk value to build an artificial neural network prediction model. Finally, a prediction model is used to predict the future security situation risk value. Experiments show that this method has good prediction accuracy and stability.
KW - bidirectional long short-term memory network
KW - network security
KW - software-defined network
KW - time series classification
UR - https://www.scopus.com/pages/publications/85094661039
U2 - 10.1109/AEECA49918.2020.9213592
DO - 10.1109/AEECA49918.2020.9213592
M3 - 会议稿件
AN - SCOPUS:85094661039
T3 - Proceedings of 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2020
SP - 475
EP - 479
BT - Proceedings of 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2020
Y2 - 25 August 2020 through 27 August 2020
ER -