Image Classifiers for Network Intrusions

03/13/2021 ∙ by David A. Noever, et al. ∙ 0

This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2's convolutional neural network architecture, the work demonstrates a 97 attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 56 importance rank, a random forest solution on subsets show the most important source-destination factors and the least important ones as mainly obscure protocols. The dataset is available on Kaggle.

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