HYBRID-CNN: An Efficient Scheme for Abnormal Flow Detection in the SDN-Based Smart Grid
Joint Authors
Wang, Liangliang
Wen, Mi
Ding, Pengpeng
Li, Jinguo
Guan, Yuyao
Source
Security and Communication Networks
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-20, 20 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-03
Country of Publication
Egypt
No. of Pages
20
Main Subjects
Information Technology and Computer Science
Abstract EN
Software-Defined Network (SDN) can improve the performance of the power communication network and better meet the control demand of the Smart Grid for its centralized management.
Unfortunately, the SDN controller is vulnerable to many potential network attacks.
The accurate detection of abnormal flow is especially important for the security and reliability of the Smart Grid.
Prior works were designed based on traditional machine learning methods, such as Support Vector Machine and Naive Bayes.
They are simple and shallow feature learning, with low accuracy for large and high-dimensional network flow.
Recently, there have been several related works designed based on Long Short-Term Memory (LSTM), and they show excellent ability on network flow analysis.
However, these methods cannot get the deep features from network flow, resulting in low accuracy.
To address the above problems, we propose a Hybrid Convolutional Neural Network (HYBRID-CNN) method.
Specifically, the HYBRID-CNN utilizes a Deep Neural Network (DNN) to effectively memorize global features by one-dimensional (1D) data and utilizes a CNN to generalize local features by two-dimensional (2D) data.
Finally, the proposed method is evaluated by experiments on the datasets of UNSW_NB15 and KDDCup 99.
The experimental results show that the HYBRID-CNN significantly outperforms existing methods in terms of accuracy and False Positive Rate (FPR), which successfully demonstrates that it can effectively detect abnormal flow in the SDN-based Smart Grid.
American Psychological Association (APA)
Ding, Pengpeng& Li, Jinguo& Wang, Liangliang& Wen, Mi& Guan, Yuyao. 2020. HYBRID-CNN: An Efficient Scheme for Abnormal Flow Detection in the SDN-Based Smart Grid. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1208746
Modern Language Association (MLA)
Ding, Pengpeng…[et al.]. HYBRID-CNN: An Efficient Scheme for Abnormal Flow Detection in the SDN-Based Smart Grid. Security and Communication Networks No. 2020 (2020), pp.1-20.
https://search.emarefa.net/detail/BIM-1208746
American Medical Association (AMA)
Ding, Pengpeng& Li, Jinguo& Wang, Liangliang& Wen, Mi& Guan, Yuyao. HYBRID-CNN: An Efficient Scheme for Abnormal Flow Detection in the SDN-Based Smart Grid. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1208746
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208746