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FedCRI: Federated Mobile Cyber-Risk Intelligence

, , , , , и . The Network and Distributed System Security Symposium (NDSS), (2022)
DOI: 10.14722/ndss.2022.23153

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Phillip Thiebes

Thomas Phillip

Phillip Schley

Phillip Inderhees

Phillip Würzinger

 

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FLAME: Taming Backdoors in Federated Learning., , , , , , , , , и 3 other автор(ы). USENIX Security Symposium, стр. 1415-1432. USENIX Association, (2022)AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms., , , , , , , и . NDSS, The Internet Society, (2023)DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection., , , и . NDSS, The Internet Society, (2022)BayBFed: Bayesian Backdoor Defense for Federated Learning., , , , и . SP, стр. 737-754. IEEE, (2023)FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning., , , , и . CoRR, (2023)FLGUARD: Secure and Private Federated Learning., , , , , , , , , и 1 other автор(ы). IACR Cryptol. ePrint Arch., (2021)ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks., , , , , и . USENIX Security Symposium, стр. 4301-4318. USENIX Association, (2023)Don't Buy the Pig in a Poke: Benchmarking DNNs Inference Performance before Development., , и . HICSS, стр. 7790-7799. ScholarSpace, (2024)SAFELearn: Secure Aggregation for private FEderated Learning., , , , , , , , , и 1 other автор(ы). SP (Workshops), стр. 56-62. IEEE, (2021)FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks., , , , и . ACSAC, стр. 647-661. ACM, (2023)