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UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers.

, , , , , , , , , , , and . USENIX Security Symposium, page 2777-2794. USENIX Association, (2021)

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Enhancing Robustness in Federated Learning by Supervised Anomaly Detection., , , and . ICPR, page 996-1003. IEEE, (2022)Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms., , , , and . ACL (1), page 12149-12163. Association for Computational Linguistics, (2023)Sensor-Based Implicit Authentication of Smartphone Users., and . DSN, page 309-320. IEEE Computer Society, (2017)Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning., and . DSN, page 297-308. IEEE Computer Society, (2017)A Large-Scale Empirical Study on the Vulnerability of Deployed IoT Devices., , , , , , , , and . IEEE Trans. Dependable Secur. Comput., 19 (3): 1826-1840 (2022)User Authentication and Security Vulnerabilities Using Smartphone Sensors and Machine Learning. Princeton University, USA, (2019)Exact Incremental and Decremental Learning for LS-SVM., , , , and . ICIP, page 2334-2338. IEEE, (2019)MOPT: Optimized Mutation Scheduling for Fuzzers., , , , , , and . USENIX Security Symposium, page 1949-1966. USENIX Association, (2019)NeuralFP: Out-of-distribution Detection using Fingerprints of Neural Networks., , , , and . ICPR, page 9561-9568. IEEE, (2020)UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers., , , , , , , , , and 2 other author(s). USENIX Security Symposium, page 2777-2794. USENIX Association, (2021)