Network Intrusion Detection Based on Semi-supervised Variational Auto-Encoder

Network intrusion detection systems (NIDSs) based on machine learning have been attracting much attention for its potential ability to detect unknown attacks that are hard for signature-based NIDSs to detect. However, acquisition of a large amount of labeled data that general supervised learning methods need is prohibitively expensive, and this results in making it hard for learning-based NIDS to become widespread in practical use.

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