Network Attack Classification and Recognition Using HMM and Improved Evidence Theory

In this paper, a decision model of fusion classification based on HMM-DS is proposed, and the training and recognition methods of the model are given. As the pure HMM classifier can’t have an ideal balance between each model with a strong ability to identify its target and the maximum difference between models. So in this paper, the results of HMM are integrated into the DS framework, and HMM provides state probabilities for DS. The output of each hidden Markov model is used as a body of evidence. The improved evidence theory method is proposed to fuse the results and encounter drawbacks of the pure HMM for improving classification accuracy of the system. We compare our approach with the traditional evidence theory method, other representative improved DS methods, pure HMM method and common classification methods. The experimental results show that our proposed method has a significant practical effect in improving the training process of network attack classification with high accuracy.

@article{Luo2016,
  title     = {Network Attack Classification and Recognition Using HMM and Improved Evidence Theory},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {4},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Gang Luo and Ya Wen and Lingyun Xiang},
  doi       = {10.14569/IJACSA.2016.070404},
  url       = {https://doi.org/10.14569/IJACSA.2016.070404}
}