FHNN: A Resampling Method for Intrusion Detection
- Resource Type
- Conference
- Authors
- Yueai, Zhao; Junjie, Chen
- Source
- 2010 WASE International Conference on Information Engineering Information Engineering (ICIE), 2010 WASE International Conference on. 2:168-171 Aug, 2010
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Bioengineering
Training
Classification algorithms
Nearest neighbor searches
Intrusion detection
Testing
Algorithm design and analysis
Sampling methods
resampling methods
imbalanced data
network intrusion detection
Neighborhood cleaning rule
Adaboost Algorithm
- Language
To improve the data processing speed of intrusion detection system, this paper focused on how to select representative samples from network data sets. Several resampling methods were discussed in this paper. The novel algorithm, Fast Hierarchical Nearest Neighbor (FHNN) outperformed NCL method in experiments with KDD’99 datasets. Taking the two-stage strategy with load balancing model for high-speed network intrusion detection system (HNIDS), we split the training dataset by the protocol and build the patterns for each dataset. Experimental results show that FHNN is faster than other methods and it is very efficient in tacking noise from majority class examples.