To detect network attacks more effectively, this study uses Honeypot techniques to collect
the latest network attack data and proposes network intrusion detection classification models,
based on deep learning, combined with DNN and LSTM models. Experiments showed that the data
set training models gave better results than the KDD CUP 99 training model’s detection rate and
false positive rate. The DNN-LSTM intrusion detection algorithm, proposed in this study, gives better
results than KDD CUP 99 training model. Compared to other algorithms, such as LeNet, DNNLSTM
intrusion detection algorithm exhibits shorter classification test time along with better accuracy
and recall rate of intrusion detection.