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Towards Real-time Network Intrusion Detection with Image-based Sequential Packets Representation
  • Jalal Ghadermazi ,
  • Ankit Shah ,
  • Nathaniel Bastian
Jalal Ghadermazi
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Ankit Shah
University of South Florida

Corresponding Author:[email protected]

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Nathaniel Bastian
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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.