Heterogeneous IoT Intrusion Detection Based on Fusion Word Embedding Deep Transfer Learning

In the context of the Internet of Everything, traditional machine learning-based intrusion detection systems (IDS) have difficulties in heterogeneous data preprocessing and fusion training, and can no longer meet the needs of heterogeneous Internet of Things (HeIoT) intrusion detection. This article proposes a deep transfer learning method based on word embedding (WE) to solve the above problems. First, a multisource data fusion method based on natural language processing is designed to maintain the consistency of the source domain and target domain tensors and complete the sample transfer. Then, use WE to map the mathematical and logical features of the physical network into feature space vectors to complete feature transfer. Finally, the heterogeneous fusion WE coefficients were imported into the deep learning model to complete the model transfer. In the simulation, the KDD CUP 99, NSL-KDD, and UNSW-NB15 datasets widely used in the IDS research field are used as the single source domain and the multisource heterogeneous fusion source domain, respectively, which verifies the effectiveness of WE combined with mainstream deep learning models in IDS. The results show that the proposed scheme can simplify the data standardization process, can better preserve data features, the WE space of heterogeneous sequences can coexist, and the convolutional neural network and bidirectional long short-term memory models perform better, the test accuracy of the single source domain and heterogeneous source domain can reach more than 98.5%, realize the integration of HeIoT network sequences, and solve the cold start problem of HeIoT IDS.