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.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.Security[#item_full_content]