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Detecting Malware Based on Dynamic Analysis Techniques Using Deep Graph Learning.

, , , , and . FDSE, volume 12466 of Lecture Notes in Computer Science, page 357-378. Springer, (2020)

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Intrusion Detection Using Extremely Limited Data Based on SDN., , , and . IEEE Conf. on Intelligent Systems, page 304-309. IEEE, (2020)Detecting Malware Based on Dynamic Analysis Techniques Using Deep Graph Learning., , , , and . FDSE, volume 12466 of Lecture Notes in Computer Science, page 357-378. Springer, (2020)Using deep graph learning to improve dynamic analysis-based malware detection in PE files., , and . J. Comput. Virol. Hacking Tech., 20 (1): 153-172 (2024)A Fresh Look at Combining Logs and Network Data to Detect Anomalous Activity., , , and . ICT-DM, page 1-6. IEEE, (2019)An Ensemble Detection Model Using Multinomial Classification of Stochastic Gas Smart Meter Data to Improve Wellbeing Monitoring in Smart Cities., , , and . IEEE Access, (2020)Towards an Approach for Fuel Poverty Detection from Gas Smart Meter Data using Decision Tree Learning., , and . IMMS, page 23-28. ACM, (2020)A Novel Deep Clustering Variational Auto-Encoder for Anomaly-based Network Intrusion Detection., , , and . KSE, page 1-7. IEEE, (2022)Critical Infrastructure Testbed for Cyber-Security Training and Research., , and . UKAIS, page 23. (2016)Deep Nested Clustering Auto-Encoder for Anomaly-Based Network Intrusion Detection., , , , and . RIVF, page 289-294. IEEE, (2023)Patient Privacy Violation Detection in Healthcare Critical Infrastructures: An Investigation Using Density-Based Benchmarking., , , and . Future Internet, 12 (6): 100 (2020)