Author : David A. Noever
Affiliation : PeopleTec, Inc., Huntsville, Alabama
Country : USA
Category : Computer Science & Information Technology
Volume, Issue, Month, Year : 11, 05, April, 2021
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.
For More Details : https://aircconline.com/csit/papers/vol11/csit110504.pdf
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