| Trojaning attack on neural networks Y Liu, S Ma, Y Aafer, WC Lee, J Zhai, W Wang, X Zhang | 793 | 2017 |
| Abs: Scanning neural networks for back-doors by artificial brain stimulation Y Liu, WC Lee, G Tao, S Ma, Y Aafer, X Zhang Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications …, 2019 | 245 | 2019 |
| Nic: Detecting adversarial samples with neural network invariant checking S Ma, Y Liu, G Tao, WC Lee, X Zhang 26th Annual Network And Distributed System Security Symposium (NDSS 2019), 2019 | 211 | 2019 |
| Protracer: Towards Practical Provenance Tracing by Alternating Between Logging and Tainting. S Ma, X Zhang, D Xu NDSS 2, 4, 2016 | 185 | 2016 |
| Badnl: Backdoor attacks against nlp models with semantic-preserving improvements X Chen, A Salem, D Chen, M Backes, S Ma, Q Shen, Z Wu, Y Zhang Annual Computer Security Applications Conference, 554-569, 2021 | 147 | 2021 |
| Attacks meet interpretability: Attribute-steered detection of adversarial samples G Tao, S Ma, Y Liu, X Zhang Advances in Neural Information Processing Systems 31, 2018 | 146 | 2018 |
| Dynamic backdoor attacks against machine learning models A Salem, R Wen, M Backes, S Ma, Y Zhang 2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P), 703-718, 2022 | 143 | 2022 |
| MODE: automated neural network model debugging via state differential analysis and input selection S Ma, Y Liu, WC Lee, X Zhang, A Grama Proceedings of the 2018 26th ACM Joint Meeting on European Software …, 2018 | 143 | 2018 |
| Hercule: Attack story reconstruction via community discovery on correlated log graph K Pei, Z Gu, B Saltaformaggio, S Ma, F Wang, Z Zhang, L Si, X Zhang, ... Proceedings of the 32Nd Annual Conference on Computer Security Applications …, 2016 | 142 | 2016 |
| MPI: Multiple Perspective Attack Investigation with Semantic Aware Execution Partitioning. S Ma, J Zhai, F Wang, KH Lee, X Zhang, D Xu USENIX Security Symposium, 1111-1128, 2017 | 111 | 2017 |
| Profuzzer: On-the-fly input type probing for better zero-day vulnerability discovery W You, X Wang, S Ma, J Huang, X Zhang, XF Wang, B Liang 2019 IEEE symposium on security and privacy (SP), 769-786, 2019 | 95 | 2019 |
| MCI: Modeling-based Causality Inference in Audit Logging for Attack Investigation. Y Kwon, F Wang, W Wang, KH Lee, WC Lee, S Ma, X Zhang, D Xu, S Jha, ... NDSS 2, 4, 2018 | 86 | 2018 |
| Accurate, low cost and instrumentation-free security audit logging for windows S Ma, KH Lee, CH Kim, J Rhee, X Zhang, D Xu Proceedings of the 31st Annual Computer Security Applications Conference …, 2015 | 81 | 2015 |
| Colo: Coarse-grained lock-stepping virtual machines for non-stop service YZ Dong, W Ye, YH Jiang, I Pratt, SQ Ma, J Li, HB Guan Proceedings of the 4th annual Symposium on Cloud Computing, 1-16, 2013 | 74 | 2013 |
| Deep feature space trojan attack of neural networks by controlled detoxification S Cheng, Y Liu, S Ma, X Zhang Proceedings of the AAAI Conference on Artificial Intelligence 35 (2), 1148-1156, 2021 | 64 | 2021 |
| Kernel-supported cost-effective audit logging for causality tracking S Ma, J Zhai, Y Kwon, KH Lee, X Zhang, G Ciocarlie, A Gehani, ... 2018 {USENIX} Annual Technical Conference ({USENIX}{ATC} 18), 241-254, 2018 | 57 | 2018 |
| Automatic model generation from documentation for Java API functions J Zhai, J Huang, S Ma, X Zhang, L Tan, J Zhao, F Qin Proceedings of the 38th International Conference on Software Engineering …, 2016 | 55 | 2016 |
| SLF: Fuzzing without valid seed inputs W You, X Liu, S Ma, D Perry, X Zhang, B Liang 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE …, 2019 | 51 | 2019 |
| Correlations between deep neural network model coverage criteria and model quality S Yan, G Tao, X Liu, J Zhai, S Ma, L Xu, X Zhang Proceedings of the 28th ACM Joint Meeting on European Software Engineering …, 2020 | 45 | 2020 |
| ATLAS: A Sequence-based Learning Approach for Attack Investigation. A Alsaheel, Y Nan, S Ma, L Yu, G Walkup, ZB Celik, X Zhang, D Xu USENIX Security Symposium, 3005-3022, 2021 | 44 | 2021 |
