An intrusion detection system using principal component analysis and time delay neural network

@article{Kang2005AnID,
  title={An intrusion detection system using principal component analysis and time delay neural network},
  author={Byoung-Doo Kang and Jae-Won Lee and Jong-Ho Kim and O-Hwa Kwon and Chi-Young Seong and Sang-Kyoon Kim},
  journal={Proceedings of 7th International Workshop on Enterprise networking and Computing in Healthcare Industry, 2005. HEALTHCOM 2005.},
  year={2005},
  pages={442-445}
}
The intrusion detection system (IDS) generally uses the misuse detection model based on rules because this model has low false alarm rates. However, the rule based IDSs are not efficient for mutated attacks, because they need additional rules for the variations of the attacks. In this paper, we propose an intrusion detection system using the principal component analysis (PCA) and the time delay neural network (TDNN). Packets on the network can be considered as gray images of which pixels… CONTINUE READING