| Trojaning attack on neural networks Y Liu, S Ma, Y Aafer, WC Lee, J Zhai, W Wang, X Zhang | 1490 | 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 | 518 | 2019 |
| Locating faults through automated predicate switching X Zhang, N Gupta, R Gupta Proceedings of the 28th international conference on Software engineering …, 2006 | 404 | 2006 |
| Automatic Protocol Format Reverse Engineering through Context-Aware Monitored Execution. Z Lin, X Jiang, D Xu, X Zhang NDSS 8, 1-15, 2008 | 361 | 2008 |
| 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, 24-27, 2019 | 326 | 2019 |
| Automatic reverse engineering of data structures from binary execution Z Lin, X Zhang, D Xu Proceedings of the 11th Annual Information Security Symposium, 5, 2010 | 326 | 2010 |
| Precise dynamic slicing algorithms X Zhang, R Gupta, Y Zhang Software Engineering, 2003. Proceedings. 25th International Conference on …, 2003 | 308 | 2003 |
| AsDroid: Detecting stealthy behaviors in android applications by user interface and program behavior contradiction J Huang, X Zhang, L Tan, P Wang, B Liang Proceedings of the 36th International Conference on Software Engineering …, 2014 | 292 | 2014 |
| Z3-str: A z3-based string solver for web application analysis Y Zheng, X Zhang, V Ganesh Proceedings of the 2013 9th Joint Meeting on Foundations of Software …, 2013 | 275 | 2013 |
| ProTracer: Towards Practical Provenance Tracing by Alternating Between Logging and Tainting S Ma, X Zhang, D Xu | 263 | 2016 |
| High Accuracy Attack Provenance via Binary-based Execution Partition. KH Lee, X Zhang, D Xu NDSS, 2013 | 263 | 2013 |
| Locating faulty code using failure-inducing chops N Gupta, H He, X Zhang, R Gupta Proceedings of the 20th IEEE/ACM international Conference on Automated …, 2005 | 261 | 2005 |
| Composite Backdoor Attack for Deep Neural Network by Mixing Existing Benign Features J Lin, L Xu, Y Liu, X Zhang Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications …, 2020 | 249 | 2020 |
| 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 | 230 | 2018 |
| Detecting Attacks Against Robotic Vehicles: A Control Invariant Approach H Choi, WC Lee, Y Aafer, F Fei, Z Tu, X Zhang, D Xu, X Xinyan Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications …, 2018 | 225 | 2018 |
| 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 | 219* | |
| Pruning dynamic slices with confidence X Zhang, N Gupta, R Gupta ACM SIGPLAN Notices 41 (6), 169-180, 2006 | 216 | 2006 |
| Experimental evaluation of using dynamic slices for fault location X Zhang, H He, N Gupta, R Gupta Proceedings of the sixth international symposium on Automated analysis …, 2005 | 213 | 2005 |
| 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 | 208 | 2016 |
| Loggc: Garbage collecting audit log KH Lee, X Zhang, D Xu Proceedings of the 2013 ACM SIGSAC conference on Computer & communications …, 2013 | 205 | 2013 |
