| Adversarial deep ensemble: Evasion attacks and defenses for malware detection D Li, Q Li IEEE Transactions on Information Forensics and Security 15, 3886-3900, 2020 | 137 | 2020 |
| A framework for enhancing deep neural networks against adversarial malware D Li, Q Li, Y Ye, S Xu IEEE Transactions on Network Science and Engineering 8 (1), 736-750, 2021 | 63* | 2021 |
| Arms race in adversarial malware detection: A survey D Li, Q Li, Y Ye, S Xu ACM Computing Surveys (CSUR) 55 (1), 1-35, 2021 | 57 | 2021 |
| Hashtran-dnn: A framework for enhancing robustness of deep neural networks against adversarial malware samples D Li, R Baral, T Li, H Wang, Q Li, S Xu arXiv preprint arXiv:1809.06498, 2018 | 29 | 2018 |
| Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and AICS'2019 Challenge D Li, Q Li, Y Ye, S Xu arXiv preprint arXiv:1812.08108, 2018 | 21 | 2018 |
| Can we leverage predictive uncertainty to detect dataset shift and adversarial examples in android malware detection? D Li, T Qiu, S Chen, Q Li, S Xu Proceedings of the 37th Annual Computer Security Applications Conference …, 2021 | 18 | 2021 |
| A robust inference algorithm for crowd sourced categorization M Wu, Q Li, J Zhang, S Cui, D Li, Y Qi 2017 12th International Conference on Intelligent Systems and Knowledge …, 2017 | 11 | 2017 |
| Pad: Towards principled adversarial malware detection against evasion attacks D Li, S Cui, Y Li, J Xu, F Xiao, S Xu IEEE Transactions on Dependable and Secure Computing, 2023 | 7 | 2023 |
| Enhancing robustness of deep neural networks against adversarial malware samples: Principles, framework, and application to AICS’2019 challenge D Li, Q Li The AAAI-19 Workshop on Artificial Intelligence for Cyber Security (AICS), 2019 | 7 | 2019 |
| Simwalk: learning network latent representations with social relation similarity S Cui, B Xia, T Li, M Wu, D Li, Q Li, H Zhang 2017 12th International Conference on Intelligent Systems and Knowledge …, 2017 | 5 | 2017 |
| Malware Evasion Attacks Against IoT and Other Devices: An Empirical Study Y Xu, D Li, Q Li, S Xu Tsinghua Science and Technology 29 (1), 127-142, 2023 | 2 | 2023 |
| Security in defect detection: A new one-pixel attack for fooling DNNs P Wang, Q Li, D Li, S Meng, M Bilal, A Mukherjee Journal of King Saud University-Computer and Information Sciences 35 (8), 101689, 2023 | 1 | 2023 |
| Hyper-Mol: Molecular Representation Learning via Fingerprint-Based Hypergraph S Cui, Q Li, D Li, Z Lian, J Hou Computational Intelligence and Neuroscience 2023, 2023 | 1 | 2023 |
