This study proposes a novel artificial intelligence-enabled
methodological framework for packet-based network intrusion detection
system that effectively analyzes header and payload data and considers
temporal connections among packets. The AI framework transforms
sequential packets into a two-dimensional image, which is then passed
through a convolutional neural network-based intrusion detector model.
Experimental results using publicly available data sets demonstrate that
the methodology can detect network attacks earlier than flow-based
approaches. It also exhibits high transferability and shows promising
resilience against adversarial examples.