An improved density peaks clustering algorithm based on grid screening and mutual neighborhood degree for network anomaly detection.
Sci Rep
; 12(1): 1409, 2022 01 26.
Article
en En
| MEDLINE
| ID: mdl-35082307
ABSTRACT
With the rapid development of network technologies and the increasing amount of network abnormal traffic, network anomaly detection presents challenges. Existing supervised methods cannot detect unknown attack, and unsupervised methods have low anomaly detection accuracy. Here, we propose a clustering-based network anomaly detection model, and then a novel density peaks clustering algorithm DPC-GS-MND based on grid screening and mutual neighborhood degree for network anomaly detection. The DPC-GS-MND algorithm utilizes grid screening to effectively reduce the computational complexity, improves the clustering accuracy through mutual neighborhood degree, and also defines a cluster center decision value for automatically selecting cluster centers. We implement complete experiments on two real-world datasets KDDCup99 and CIC-IDS-2017, and the experimental results demonstrated that the proposed DPC-GS-MND can detect network anomaly traffic with higher accuracy and efficiency. Together, it has a good application prospect in the network anomaly detection system in complex network environments.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Idioma:
En
Revista:
Sci Rep
Año:
2022
Tipo del documento:
Article
País de afiliación:
China