IEICE TRANSACTIONS on Communications
Anomaly Detection of Network Traffic Based on Intuitionistic Fuzzy Set Ensemble
Summary :
In order to improve the anomaly detection efficiency of network traffic, firstly, the model is established for network flows based on complex networks. Aiming at the uncertainty and fuzziness between network traffic characteristics and network states, the deviation extent is measured from the normal network state using deviation interval uniformly, and the intuitionistic fuzzy sets (IFSs) are established for the various characteristics on the network model that the membership degree, non-membership degree and hesitation margin of the IFSs are used to quantify the ownership of values to be tested and the corresponding network state. Then, the knowledge measure (KM) is introduced into the intuitionistic fuzzy weighted geometry (IFWGω) to weight the results of IFSs corresponding to the same network state with different characteristics together to detect network anomaly comprehensively. Finally, experiments are carried out on different network traffic datasets to analyze the evaluation indicators of network characteristics by our method, and compare with other existing anomaly detection methods. The experimental results demonstrate that the changes of various network characteristics are inconsistent under abnormal attack, and the accuracy of anomaly detection results obtained by our method is higher, verifying our method has a better detection performance.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E106-B No.7 pp.538-546
- Publication Date
- 2023/07/01
- Publicized
- 2023/01/13
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2022EBP3147
- Type of Manuscript
- PAPER
- Category
- Fundamental Theories for Communications
Authors
He TIAN
Liaoning University,Liaoning Institute of Science and Technology
Kaihong GUO
Liaoning University
Xueting GUAN
Liaoning University
Zheng WU
Liaoning University
Keyword
Latest Issue
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He TIAN, Kaihong GUO, Xueting GUAN, Zheng WU, "Anomaly Detection of Network Traffic Based on Intuitionistic Fuzzy Set Ensemble" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 7, pp. 538-546, July 2023, doi: 10.1587/transcom.2022EBP3147.
Abstract: In order to improve the anomaly detection efficiency of network traffic, firstly, the model is established for network flows based on complex networks. Aiming at the uncertainty and fuzziness between network traffic characteristics and network states, the deviation extent is measured from the normal network state using deviation interval uniformly, and the intuitionistic fuzzy sets (IFSs) are established for the various characteristics on the network model that the membership degree, non-membership degree and hesitation margin of the IFSs are used to quantify the ownership of values to be tested and the corresponding network state. Then, the knowledge measure (KM) is introduced into the intuitionistic fuzzy weighted geometry (IFWGω) to weight the results of IFSs corresponding to the same network state with different characteristics together to detect network anomaly comprehensively. Finally, experiments are carried out on different network traffic datasets to analyze the evaluation indicators of network characteristics by our method, and compare with other existing anomaly detection methods. The experimental results demonstrate that the changes of various network characteristics are inconsistent under abnormal attack, and the accuracy of anomaly detection results obtained by our method is higher, verifying our method has a better detection performance.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2022EBP3147/_p
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@ARTICLE{e106-b_7_538,
author={He TIAN, Kaihong GUO, Xueting GUAN, Zheng WU, },
journal={IEICE TRANSACTIONS on Communications},
title={Anomaly Detection of Network Traffic Based on Intuitionistic Fuzzy Set Ensemble},
year={2023},
volume={E106-B},
number={7},
pages={538-546},
abstract={In order to improve the anomaly detection efficiency of network traffic, firstly, the model is established for network flows based on complex networks. Aiming at the uncertainty and fuzziness between network traffic characteristics and network states, the deviation extent is measured from the normal network state using deviation interval uniformly, and the intuitionistic fuzzy sets (IFSs) are established for the various characteristics on the network model that the membership degree, non-membership degree and hesitation margin of the IFSs are used to quantify the ownership of values to be tested and the corresponding network state. Then, the knowledge measure (KM) is introduced into the intuitionistic fuzzy weighted geometry (IFWGω) to weight the results of IFSs corresponding to the same network state with different characteristics together to detect network anomaly comprehensively. Finally, experiments are carried out on different network traffic datasets to analyze the evaluation indicators of network characteristics by our method, and compare with other existing anomaly detection methods. The experimental results demonstrate that the changes of various network characteristics are inconsistent under abnormal attack, and the accuracy of anomaly detection results obtained by our method is higher, verifying our method has a better detection performance.},
keywords={},
doi={10.1587/transcom.2022EBP3147},
ISSN={1745-1345},
month={July},}
Copy
TY - JOUR
TI - Anomaly Detection of Network Traffic Based on Intuitionistic Fuzzy Set Ensemble
T2 - IEICE TRANSACTIONS on Communications
SP - 538
EP - 546
AU - He TIAN
AU - Kaihong GUO
AU - Xueting GUAN
AU - Zheng WU
PY - 2023
DO - 10.1587/transcom.2022EBP3147
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 7
JA - IEICE TRANSACTIONS on Communications
Y1 - July 2023
AB - In order to improve the anomaly detection efficiency of network traffic, firstly, the model is established for network flows based on complex networks. Aiming at the uncertainty and fuzziness between network traffic characteristics and network states, the deviation extent is measured from the normal network state using deviation interval uniformly, and the intuitionistic fuzzy sets (IFSs) are established for the various characteristics on the network model that the membership degree, non-membership degree and hesitation margin of the IFSs are used to quantify the ownership of values to be tested and the corresponding network state. Then, the knowledge measure (KM) is introduced into the intuitionistic fuzzy weighted geometry (IFWGω) to weight the results of IFSs corresponding to the same network state with different characteristics together to detect network anomaly comprehensively. Finally, experiments are carried out on different network traffic datasets to analyze the evaluation indicators of network characteristics by our method, and compare with other existing anomaly detection methods. The experimental results demonstrate that the changes of various network characteristics are inconsistent under abnormal attack, and the accuracy of anomaly detection results obtained by our method is higher, verifying our method has a better detection performance.
ER -
English
