Image Classifiers for Network Intrusions


David A. Noever and Samantha E. Miller Noever, PeopleTec, Inc., USA


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 sourcedestination factors and the least important ones as mainly obscure protocols. The dataset is available on Kaggle.


Neural Networks, Computer Vision, Image Classification, Intrusion Detection, MNIST Benchmark.

Full Text  Volume 11, Number 5