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An interpretable semi-supervised system for detecting cyberattacks using anomaly detection in industrial scenarios., , , and . IET Inf. Secur., 17 (4): 553-566 (July 2023)A Supervised ML Biometric Continuous Authentication System for Industry 4.0., , , , and . IEEE Trans. Ind. Informatics, 18 (12): 9132-9140 (2022)SeCoMan: A Semantic-Aware Policy Framework for Developing Privacy-Preserving and Context-Aware Smart Applications., , , and . IEEE Syst. J., 10 (3): 1111-1124 (2016)Mitigation of cyber threats: Protection mechanisms in federated SDN/NFV infrastructures for 5G within FIRE+., , , , , , , , and . Concurr. Comput. Pract. Exp., 33 (7): 1 (2021)VAASI: Crafting valid and abnormal adversarial samples for anomaly detection systems in industrial scenarios., , , and . J. Inf. Secur. Appl., (December 2023)Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges., , , , , , , and . IEEE Commun. Surv. Tutorials, 25 (4): 2983-3013 (Fourthquarter 2023)Moving Target Defense Strategy Selection against Malware in Resource-Constrained Devices., , , , , and . CSR, page 123-129. IEEE, (2023)Can Evil IoT Twins Be Identified? Now Yes, a Hardware Behavioral Fingerprinting Methodology., , , , , and . CoRR, (2021)TemporalFED: Detecting Cyberattacks in Industrial Time-Series Data Using Decentralized Federated Learning., , , and . CoRR, (2023)Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario., , , , , , and . CoRR, (2022)