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Threat detection and investigation with system-level provenance graphs: A survey.

, , , , and . Comput. Secur., (2021)

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Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking., , , , , and . CoRR, (2019)Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack., , , , , and . CoRR, (2020)Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction., , , , and . CoRR, (2022)On Robustness of Lane Detection Models to Physical-World Adversarial Attacks in Autonomous Driving., and . CoRR, (2021)Impact Evaluation of Falsified Data Attacks on Connected Vehicle Based Traffic Signal Control., , , , , and . CoRR, (2020)An Initial Investigation of Protocol Customization., , and . FEAST@CCS, page 57-64. ACM, (2017)No One In The Middle: Enabling Network Access Control Via Transparent Attribution., , , , , and . AsiaCCS, page 651-658. ACM, (2018)Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack., , , , , and . USENIX Security Symposium, page 3309-3326. USENIX Association, (2021)QoE Inference Without Application Control., , , , and . Internet-QoE@SIGCOMM, page 19-24. ACM, (2016)AVMaestro: A Centralized Policy Enforcement Framework for Safe Autonomous-driving Environments., , , , , , , and . IV, page 1333-1339. IEEE, (2022)