| Title | ELM Network Intrusion Detection Model Based on SLPP Feature Extraction |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Jingyi, Wu, Xusheng, Gan, Jieli, Huang, Shenghou, Li |
| Conference Name | 2021 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) |
| Keywords | composability, Computational modeling, extreme learning machine, feature extraction, Metrics, network intrusion detection, Neural networks, pubcrawl, resilience, Resiliency, simulation, Supervised Locality Preserving Projections, Support vector machines, Training |
| Abstract | To improve the safety precaution level of network system, a combined network intrusion detection method is proposed based on Supervised Locality Preserving Projections (SLPP) feature extraction and Extreme Learning Machine (ELM). In this method, the feature extraction capability of SLPP is first used to reduce the dimensionality of the original network connection and system audit data, and get a feature set, then, based on this, the advantages of ELM in pattern recognition is adopted to build a network intrusion detection model for detecting and determining intrusion behavior. Simulation results show that, under the same experiment conditions, compared with traditional neural networks and support vector machines, the proposed method has more advantages in training efficiency and generalization performance. |
| DOI | 10.1109/ICPICS52425.2021.9524271 |
| Citation Key | jingyi_elm_2021 |
ELM Network Intrusion Detection Model Based on SLPP Feature Extraction