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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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A Large-Scale Empirical Study on the Vulnerability of Deployed IoT Devices., , , , , , , , and . IEEE Trans. Dependable Secur. Comput., 19 (3): 1826-1840 (2022)Model Pruning Enables Efficient Federated Learning on Edge Devices., , , , , , and . IEEE Trans. Neural Networks Learn. Syst., 34 (12): 10374-10386 (December 2023)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)Implicit Sensor-based Authentication of Smartphone Users with Smartwatch., and . HASP@ISCA, page 9:1-9:8. ACM, (2016)Inferring Smartphone Users' Handwritten Patterns by using Motion Sensors., , , and . ICISSP, page 139-148. SciTePress, (2018)NeuralFP: Out-of-distribution Detection using Fingerprints of Neural Networks., , , , and . ICPR, page 9561-9568. IEEE, (2020)MOPT: Optimized Mutation Scheduling for Fuzzers., , , , , , and . USENIX Security Symposium, page 1949-1966. USENIX Association, (2019)