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An Explainable Machine Learning-based Network Intrusion Detection System for Enabling Generalisability in Securing IoT Networks., , and . CoRR, (2021)Feature Analysis for ML-based IIoT Intrusion Detection., , and . CoRR, (2021)Exploring Edge TPU for Network Intrusion Detection in IoT., , , , and . CoRR, (2021)Towards a Standard Feature Set for Network Intrusion Detection System Datasets., , and . Mob. Networks Appl., 27 (1): 357-370 (2022)E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT., , , , and . NOMS, page 1-9. IEEE, (2022)Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-Based Network Intrusion Detection., , and . Big Data Res., (2022)From zero-shot machine learning to zero-day attack detection., , , and . Int. J. Inf. Sec., 22 (4): 947-959 (2023)HBFL: A hierarchical blockchain-based federated learning framework for collaborative IoT intrusion detection., , , and . Comput. Electr. Eng., (2022)Cyber Threat Intelligence Sharing Scheme Based on Federated Learning for Network Intrusion Detection., , , and . J. Netw. Syst. Manag., 31 (1): 3 (2023)XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics., , , and . CoRR, (2022)