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- Title
Application of GA-WELM Model Based on Stratified Cross-Validation in Intrusion Detection.
- Authors
Chen, Chen; Guo, Xiangke; Zhang, Wei; Zhao, Yanzhao; Wang, Biao; Ma, Biao; Wei, Dan
- Abstract
Aiming at the problem of poor detection performance under the environment of imbalanced type distribution, an intrusion detection model of genetic algorithm to optimize weighted extreme learning machine based on stratified cross-validation (SCV-GA-WELM) is proposed. In order to solve the problem of imbalanced data types in cross-validation subsets, SCV is used to ensure that the data distribution in all subsets is consistent, thus avoiding model over-fitting. The traditional fitness function cannot solve the problem of small sample classification well. By designing a weighted fitness function and giving high weight to small sample data, the performance of the model can be effectively improved in the environment of imbalanced type distribution. The experimental results show that this model is superior to other intrusion detection models in recall and McNemar hypothesis test. In addition, the recall of the model for small sample data is higher, reaching 91.5% and 95.1%, respectively. This shows that it can effectively detect intrusions in an environment with imbalanced type distribution. Therefore, the model has practical application value in the field of intrusion detection, and can be used to improve the performance of intrusion detection systems in the actual environment. This method has a wide application prospect, such as network security, industrial control system, and power system.
- Subjects
INTRUSION detection systems (Computer security); MACHINE learning; INDUSTRIAL controls manufacturing; COMPUTER network security; GENETIC algorithms; GENETIC models
- Publication
Symmetry (20738994), 2023, Vol 15, Issue 9, p1719
- ISSN
2073-8994
- Publication type
Article
- DOI
10.3390/sym15091719