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Few-Shot Learning with Discriminative Representation for Cyberattack Detection.

, , and . KSE, page 1-6. IEEE, (2023)

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Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network., , , and . CoRR, (2017)Few-Shot Learning with Discriminative Representation for Cyberattack Detection., , and . KSE, page 1-6. IEEE, (2023)Feature Representation of AutoEncoders for Unsupervised IoT Malware Detection., , , and . FDSE, volume 13076 of Lecture Notes in Computer Science, page 272-290. Springer, (2021)Learning Latent Distribution for Distinguishing Network Traffic in Intrusion Detection System., , , , , and . ICC, page 1-6. IEEE, (2019)Learning Latent Representation for IoT Anomaly Detection., , , , , and . IEEE Trans. Cybern., 52 (5): 3769-3782 (2022)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)One-Class Classification for Anomaly Detection with Kernel Density Estimation and Genetic Programming., , and . EuroGP, volume 9594 of Lecture Notes in Computer Science, page 3-18. Springer, (2016)Semantics Based Substituting Technique for Reducing Code Bloat in Genetic Programming., , and . SoICT, page 77-83. ACM, (2018)Late-acceptance and step-counting hill-climbing GP for anomaly detection., , and . GECCO (Companion), page 221-222. ACM, (2017)IoT Malware Detection based on Latent Representation., , , and . KSE, page 177-182. IEEE, (2020)