Autoencoder-based Unsupervised Intrusion Detection using Multi-Scale Convolutional Recurrent Networks. (arXiv:2204.03779v1 [cs.CR])

The massive growth of network traffic data leads to a large volume of
datasets. Labeling these datasets for identifying intrusion attacks is very
laborious and error-prone. Furthermore, network traffic data have complex
time-varying non-linear relationships. The existing state-of-the-art intrusion
detection solutions use a combination of various supervised approaches along
with fused features subsets based on correlations in traffic data. These
solutions often require high computational cost, manual support in fine-tuning
intrusion detection models, and labeling of data that limit real-time …

detection intrusion intrusion detection networks scale