  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% accuracy in distinguishing normal and
attack traffic. Further class refinements to 9 individual attack families
(exploits, worms, shellcodes) show an overall 56% accuracy. Using feature
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.
