A New Intrusion Detection model based on GRU and Salient Feature Approach

EasyChair Preprint 1624

Abstract

Gated Recurrent Unit (GRU) is a variant of a recurrent neural network, just like an LSTM network. Compared with RNN, the two networks have higher accuracy in processing sequence problems, and both of them have been proven to be effective in varieties of machine learning tasks such as natural language processing, text classification and speech recognition. In addition, the network unit structure of the GRU is simpler than the LSTM unit structure, which is more conducive to the training of the model. NSL-KDD datasets, which is the replacement of KDD cup 99, is still one of the datasets for measuring the effectiveness of intrusion detection models. In order to reduce the feature data dimension and combine the prior knowledge of computer network, a GRU intrusion detection method based on salient features (SF-GRU) is proposed. SF-GRU selects the distinctive features of response for different intrusion forms, and uses GRU network to identify the selected features to improve the efficiency of model detection. The experimental results show that compared with the traditional deep learning method, this proposal has higher accuracy and computational efficiency.

Keyphrases: Gate Recurrent Unit, Intrusion Detection, Salient Feature selection, prior knowledge

@booklet{EasyChair:1624,
  author    = {Jian Hou and Fangai Liu and Xuqiang Zhuang},
  title     = {A New Intrusion Detection model based on GRU and Salient Feature Approach},
  howpublished = {EasyChair Preprint 1624},
  year      = {EasyChair, 2019}}