| Twitter spam detection based on deep learning T Wu, S Liu, J Zhang, Y Xiang Proceedings of the australasian computer science week multiconference, 1-8, 2017 | 76 | 2017 |
| Addressing the class imbalance problem in Twitter spam detection using ensemble learning S Liu, Y Wang, J Zhang, C Chen, Y Xiang Computers & Security 69, 35-49, 2017 | 56 | 2017 |
| An ensemble oversampling model for class imbalance problem in software defect prediction S Huda, K Liu, M Abdelrazek, A Ibrahim, S Alyahya, H Al-Dossari, ... IEEE access 6, 24184-24195, 2018 | 51 | 2018 |
| Fuzzy-based information decomposition for incomplete and imbalanced data learning S Liu, J Zhang, Y Xiang, W Zhou IEEE Transactions on Fuzzy Systems 25 (6), 1476-1490, 2017 | 28 | 2017 |
| Dynamic access point association using software defined networking K Sood, S Liu, S Yu, Y Xiang 2015 International Telecommunication Networks and Applications Conference …, 2015 | 28 | 2015 |
| Statistical Detection of Online Drifting Twitter Spam S Liu, J Zhang, Y Xiang Proceedings of the 11th ACM on Asia Conference on Computer and …, 2016 | 25 | 2016 |
| A comparative study of the class imbalance problem in Twitter spam detection C Li, S Liu Concurrency and Computation: Practice and Experience 30 (5), e4281, 2018 | 23 | 2018 |
| Detecting spamming activities in twitter based on deeplearning technique T Wu, S Wen, S Liu, J Zhang, Y Xiang, M Alrubaian, MM Hassan Concurrency and Computation: Practice and Experience 29 (19), e4209, 2017 | 17 | 2017 |
| DeepBalance: Deep-learning and fuzzy oversampling for vulnerability detection S Liu, G Lin, QL Han, S Wen, J Zhang, Y Xiang IEEE Transactions on Fuzzy Systems 28 (7), 1329-1343, 2019 | 14 | 2019 |
| Fuzzy-based feature and instance recovery S Liu, J Zhang, Y Wang, Y Xiang Asian Conference on Intelligent Information and Database Systems, 605-615, 2016 | 12 | 2016 |
| An ensemble learning approach for addressing the class imbalance problem in Twitter spam detection S Liu, Y Wang, C Chen, Y Xiang Australasian Conference on Information Security and Privacy, 215-228, 2016 | 11 | 2016 |
| A performance evaluation of deeplearnt features for software vulnerability detection X Ban, S Liu, C Chen, C Chua Concurrency and Computation: Practice and Experience 31 (19), e5103, 2019 | 10 | 2019 |
| An overview of attacks and defences on intelligent connected vehicles M Dibaei, X Zheng, K Jiang, S Maric, R Abbas, S Liu, Y Zhang, Y Deng, ... arXiv preprint arXiv:1907.07455, 2019 | 10 | 2019 |
| Cyber vulnerability intelligence for Internet of Things binary S Liu, M Dibaei, Y Tai, C Chen, J Zhang, Y Xiang IEEE Transactions on Industrial Informatics 16 (3), 2154-2163, 2019 | 9 | 2019 |
| Decision-based evasion attacks on tree ensemble classifiers F Zhang, Y Wang, S Liu, H Wang World Wide Web 23 (5), 2957-2977, 2020 | 8 | 2020 |
| Information-decomposition-model-based missing value estimation for not missing at random dataset S Liu, H Dai, M Gan International Journal of Machine Learning and Cybernetics 9 (1), 85-95, 2018 | 7 | 2018 |
| CD-VulD: Cross-domain vulnerability discovery based on deep domain adaptation S Liu, G Lin, L Qu, J Zhang, O De Vel, P Montague, Y Xiang IEEE Transactions on Dependable and Secure Computing, 2020 | 6 | 2020 |
| Attacks and defences on intelligent connected vehicles: A survey M Dibaei, X Zheng, K Jiang, R Abbas, S Liu, Y Zhang, Y Xiang, S Yu Digital Communications and Networks, 2020 | 4 | 2020 |
| A data-driven attack against support vectors of svm S Liu, J Zhang, Y Wang, W Zhou, Y Xiang, OD Vel Proceedings of the 2018 on Asia Conference on Computer and Communications …, 2018 | 4 | 2018 |
| Examination of reliability of missing value recovery in data mining S Liu, H Dai 2014 IEEE International Conference on Data Mining Workshop, 306-313, 2014 | 4 | 2014 |
Yang XiangIEEE Fellow, Professfor, Swinburne University of Technology在 swin.edu.au 的电子邮件经过验证
