Research on network intrusion detection based on triple sparse-LSTM

In today's world, network technology has brought great convenience to people's lives, but network security issues have become increasingly prominent and become an important research topic. Network intrusion detection mainly ensures network security by identifying network behavior, and its application is becoming increasingly widespread. This article uses the triplet sparse matrix method to process the KDDCUP99 Dataset and models it using an LSTM network. After experimental comparison and analysis of the detection performance of the Triple Sparse LSTM model and LSTM model, it was found that the two models have similar detection performance, but the Triple Sparse LSTM model has better network convergence performance and learning ability.