| Reinforcement learning algorithms for adaptive cyber defense against heartbleed M Zhu, Z Hu, P Liu Proceedings of the first ACM workshop on moving target defense, 51-58, 2014 | 62 | 2014 |
| Windranger: A directed greybox fuzzer driven by deviation basic blocks Z Du, Y Li, Y Liu, B Mao Proceedings of the 44th International Conference on Software Engineering …, 2022 | 39 | 2022 |
| Online algorithms for adaptive cyber defense on bayesian attack graphs Z Hu, M Zhu, P Liu Proceedings of the 2017 Workshop on moving target defense, 99-109, 2017 | 29 | 2017 |
| Adaptive cyber defense against multi-stage attacks using learning-based POMDP Z Hu, M Zhu, P Liu ACM Transactions on Privacy and Security (TOPS) 24 (1), 1-25, 2020 | 26 | 2020 |
| Sok: On the semantic ai security in autonomous driving J Shen, N Wang, Z Wan, Y Luo, T Sato, Z Hu, X Zhang, S Guo, Z Zhong, ... arXiv preprint arXiv:2203.05314, 2022 | 25 | 2022 |
| Detecting multi-sensor fusion errors in advanced driver-assistance systems Z Zhong, Z Hu, S Guo, X Zhang, Z Zhong, B Ray proceedings of the 31st ACM SIGSOFT International Symposium on Software …, 2022 | 17 | 2022 |
| Coverage-based scene fuzzing for virtual autonomous driving testing Z Hu, S Guo, Z Zhong, K Li arXiv preprint arXiv:2106.00873, 2021 | 17 | 2021 |
| On convergence rates of game theoretic reinforcement learning algorithms Z Hu, M Zhu, P Chen, P Liu Automatica 104, 90-101, 2019 | 15* | 2019 |
| Detecting safety problems of multi-sensor fusion in autonomous driving Z Zhong, Z Hu, S Guo, X Zhang, Z Zhong, B Ray arXiv preprint arXiv:2109.06404, 2021 | 11 | 2021 |
| ROPNN: Detection of ROP payloads using deep neural networks X Li, Z Hu, Y Fu, P Chen, M Zhu, P Liu arXiv preprint arXiv:1807.11110, 2018 | 10 | 2018 |
| Reinforcement learning for adaptive cyber defense against zero-day attacks Z Hu, P Chen, M Zhu, P Liu Adversarial and Uncertain Reasoning for Adaptive Cyber Defense: Control-and …, 2019 | 9 | 2019 |
| What you see is not what you get! thwarting just-in-time rop with chameleon P Chen, J Xu, Z Hu, X Xing, M Zhu, B Mao, P Liu 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems …, 2017 | 9 | 2017 |
| Quantifying DNN model robustness to the real-world threats Z Zhong, Z Hu, X Chen 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems …, 2020 | 6 | 2020 |
| Feedback control can make data structure layout randomization more cost-effective under zero-day attacks P Chen, Z Hu, J Xu, M Zhu, P Liu Cybersecurity 1, 1-13, 2018 | 6 | 2018 |
| Disclosing the fragility problem of virtual safety testing for autonomous driving systems Z Hu, S Guo, Z Zhong, K Li 2021 IEEE International Symposium on Software Reliability Engineering …, 2021 | 5 | 2021 |
| Towards practical robustness improvement for object detection in safety-critical scenarios Z Hu, Z Zhong International Workshop on Deployable Machine Learning for Security Defense …, 2020 | 5 | 2020 |
| A co-design adaptive defense scheme with bounded security damages against Heartbleed-like attacks Z Hu, P Chen, M Zhu, P Liu IEEE Transactions on Information Forensics and Security 16, 4691-4704, 2021 | 4 | 2021 |
| DeepReturn: A deep neural network can learn how to detect previously-unseen ROP payloads without using any heuristics X Li, Z Hu, H Wang, Y Fu, P Chen, M Zhu, P Liu Journal of Computer Security 28 (5), 499-523, 2020 | 4 | 2020 |
| PASS: A system-driven evaluation platform for autonomous driving safety and security Z Hu, J Shen, S Guo, X Zhang, Z Zhong, QA Chen, K Li NDSS Workshop on Automotive and Autonomous Vehicle Security (AutoSec), 2022 | 3 | 2022 |
| MTD Techniques for Memory Protection Against Zero-Day Attacks P Chen, Z Hu, J Xu, M Zhu, R Erbacher, S Jajodia, P Liu Adversarial and Uncertain Reasoning for Adaptive Cyber Defense: Control-and …, 2019 | 1 | 2019 |
Peng LiuRaymond G. Tronzo, M.D. Professor of Cybersecurity, Penn State UniversityBestätigte E-Mail-Adresse bei ist.psu.edu
