Using multimodal deep learning to detect malicious traffic with noisy labels

The success of a deep learning-based network intrusion detection systems (NIDS) relies on large-scale, labeled, realistic traffic. However, automated labeling of realistic traffic, such as by sand-box and rule-based approaches, is prone to errors, which in turn affects deep learning-based NIDS.

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