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Don't fool Me!: Detection, Characterisation and Diagnosis of Spoofed and Masked Events in Wireless Sensor Networks., , and . IEEE Trans. Dependable Secur. Comput., 14 (3): 279-293 (2017)Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Neural Networks., , and . CoRR, (2018)Label Sanitization Against Label Flipping Poisoning Attacks., , and . Nemesis/UrbReas/SoGood/IWAISe/GDM@PKDD/ECML, volume 11329 of Lecture Notes in Computer Science, page 5-15. Springer, (2018)Universal Adversarial Robustness of Texture and Shape-Biased Models., , , , and . ICIP, page 799-803. IEEE, (2021)Defending against poisoning attacks in online learning settings., , and . ESANN, (2019)Non-IID data re-balancing at IoT edge with peer-to-peer federated learning for anomaly detection., , , and . WISEC, page 153-163. ACM, (2021)Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection., , , and . CoRR, (2016)Regularisation Can Mitigate Poisoning Attacks: A Novel Analysis Based on Multiobjective Bilevel Optimisation., , , and . CoRR, (2020)Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks., , , and . ACM Conference on Computer and Communications Security, page 275-289. ACM, (2019)Real-time Detection of Practical Universal Adversarial Perturbations., , , and . CoRR, (2021)