Intrusion Detection Method Based on Stacked Sparse Autoencoder and Sliced GRU for Connected Healthcare Systems

Connected healthcare systems face more and more cyber attacks recently. With the development of technology, people use intrusion detection systems (IDS) to detect network attacks and achieve effective results. The existing methods do not take into account the limited storage and computing power of wireless devices on connected healthcare systems. IDSs in the connected healthcare systems need to be real-time and lightweight. This paper proposes an IDS method based on stacked sparse autoencoder (sSAE) and sliced gated recurrent unit (SGRU). The sSAE reduces the dimensionality of the original traffic data and the memory required to calculate the covariance matrix. We slice the original data and input the processed data into the SGRU networks which are paralleled. Therefore, SGRU networks achieve real-time response. The method uses the AWID dataset. The experimental results show that our scheme is better than deep neural network (DNN), recurrent neural network (RNN), long short-term memory (LSTM) and other methods. Especially, the F1-score of the method is 2–5% higher than existing schemes, the detection time is 5–13 times shorter than other solutions, and the model size is smaller than the size of other models by at least 4 times.