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Graph Neural Network-based Android Malware Classification with Jumping Knowledge.

, , , , и . DSC, стр. 1-9. IEEE, (2022)

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Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-Based Network Intrusion Detection., , и . Big Data Res., (2022)HBFL: A hierarchical blockchain-based federated learning framework for collaborative IoT intrusion detection., , , и . Comput. Electr. Eng., (2022)XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics., , , и . CoRR, (2022)NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems., , , и . BDTA/WiCON, том 371 из Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, стр. 117-135. Springer, (2020)DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection., , , , и . CCGridW, стр. 1-7. IEEE, (2023)FlowTransformer: A transformer framework for flow-based network intrusion detection systems., , , , , и . Expert Syst. Appl., (2024)Graph Neural Network-based Android Malware Classification with Jumping Knowledge., , , , и . DSC, стр. 1-9. IEEE, (2022)E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT., , , , и . NOMS, стр. 1-9. IEEE, (2022)Feature extraction for machine learning-based intrusion detection in IoT networks., , , , и . Digit. Commun. Networks, 10 (1): 205-216 (2024)