Unified Multimodal Network Intrusion Detection Systems Dataset

The Unified Multimodal Network Intrusion Detection System (UM-NIDS) dataset is a comprehensive, standardized dataset that integrates network flow data, packet payload information, and contextual features, making it highly suitable for machine learning-based intrusion detection models. This dataset addresses key limitations in existing NIDS datasets, such as inconsistent feature sets and the lack of payload or time-window-based contextual features. UM-NIDS was created by processing raw PCAP files from four well-established datasets: CIC-IDS 2017, CIC-IoT 2023, UNSW-NB15, and a CIC-DDoS 2019 dataset. By offering both flow-level and payload-based data, along with contextual features that capture historical and temporal patterns in network traffic, UM-NIDS enables comprehensive analysis and robust model development. It is ideal for cross-dataset validation and supports flexible customization, allowing users to add new datasets or adjust configurations for their specific research needs.