Security application of intrusion detection model based on deep learning in english online education

Nowadays, the issue of English online education security has become increasingly prominent. The increasing complexity and concealment of cyber-attack cause significant financial losses in English online education application, exacerbating the distrust of teachers and students towards cyberspace. Therefore, this paper proposes a multi scale convolutional neural network based on multi head attention mechanism and hierarchical long short term memory network (MCNN-MHA-HLSTM). This model uses one dimensional convolution to construct a multi scale convolution structure to extract network data feature information of different scales. And it combines multi head attention mechanism to enhance the weight of features related to English online education data, for improving the intrusion detection capability in English online education. Simultaneously it designs a hierarchical long short term memory network (HLSTM) to extract temporal features across multiple temporal hierarchical structure on network data sequences. Finally, the experimental results display that MCNN-MHA-HLSTM can significantly improve the intrusion detection capability of English online education platforms, laying a technical foundation for the operation and sustainable development of English online education security application. • This paper proposes a model to address the issue of insufficient detection performance in traditional intrusion detection methods. • The model better learns long distance sequence patterns in online education data and improves the weight of features related to online education categories. • Experimental comparisons are conducted on multiple network intrusion detection datasets.