Internet of Things data intrusion detection under GRU-LSTM algorithm

Abstract

At present, the technology of the Internet of Things is not yet perfect. Due to the limited resources of nodes in the network, it is vulnerable to attacks and various possible attack methods, which pose great challenges to the information security and privacy of the Internet of Things. How to quickly identify intrusion behaviors in the Internet of Things environment is an urgent problem that needs to be solved in current network security. Based on the working characteristics of Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) networks, this article optimizes LSTM to obtain GRU (Gate Recurrent Unit) network, and compares it with traditional logistic regression (Softmax) classification methods. The research results of this article show that using dropout to train neural networks can weaken the interaction between neurons and effectively avoid overfitting. The accuracy of GRU-LSTM and GRU-Softmax in the IoT-23 (Internet of Things) database is 96% and 76%, respectively. This article proposes an IoT data intrusion detection method based on the GRU-LSTM algorithm, which has more advantages.