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Comparison of Shallow and Deep Neural Networks in Network Intrusion Detection
In this thesis, the concept of neural networks, a form of machine learning, is applied to network intrusion detection to compare the performance of shallow and deep neural networks. Neural networks provide a robust method of machine learning that can identify patterns and classify observations or objects. Several structures of shallow and deep networks are tested with varying hyperparameters to create a good understanding of the performance and capability of each type of network. Using currently available labeled datasets, our experiments and evaluations show that shallow neural networks are able to more successfully identify malicious network traffic than the more complex deep neural networks, with the former achieving a peak average performance of 98.50% detection rate and the latter only reaching an average high of 48.30% detection rate.
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