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This research recasts the network attack dataset from UNSW-NB15 as anintrusion detection problem in image space. Using one-hot-encodings, theresulting grayscale thumbnails provide a quarter-million examples for deeplearning algorithms. Applying the MobileNetV2's convolutional neural networkarchitecture, the work demonstrates a 97% accuracy in distinguishing normal andattack traffic. Further class refinements to 9 individual attack families(exploits, worms, shellcodes) show an overall 56% accuracy. Using featureimportance rank, a random forest solution on subsets show the most importantsource-destination factors and the least important ones as mainly obscureprotocols. The dataset is available on Kaggle.

Image Classifiers for Network Intrusions