Thursday, September 30, 2021


Author :  David A. Noever

Affiliation :  PeopleTec, Inc., Huntsville, Alabama

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 05, April, 2021

Abstract :

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.

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

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