| Generating multi-categorical samples with generative adversarial networks R Camino, C Hammerschmidt, R State arXiv preprint arXiv:1807.01202, 2018 | 85 | 2018 |
| Improving missing data imputation with deep generative models RD Camino, R Hammerschmidt, CA, State arXiv preprint arXiv:1902.10666, 2019 | 62* | 2019 |
| BotGM: Unsupervised graph mining to detect botnets in traffic flows S Lagraa, J François, A Lahmadi, M Miner, C Hammerschmidt, R State Cyber Security in Networking Conference (CSNet), 2017 1st, 1-8, 2017 | 38 | 2017 |
| flexfringe: A Passive Automaton Learning Package SE Verwer, C Hammerschmidt Software Maintenance and Evolution (ICSME), 2017 IEEE International …, 2017 | 35 | 2017 |
| Radu State. Improving missing data imputation with deep generative models RD Camino, CA Hammerschmidt arXiv preprint arXiv:1902.10666, 2019 | 23* | 2019 |
| Learning behavioral fingerprints from Netflows using Timed Automata G Pellegrino, Q Lin, C Hammerschmidt, S Verwer Integrated Network and Service Management (IM), 2017 IFIP/IEEE Symposium on …, 2017 | 22 | 2017 |
| Short-term time series forecasting with regression automata Q Lin, C Hammerschmidt, G Pellegrino, S Verwer ACM SIGKDD 2016 Workshop on Mining and Learning from Time Series (MiLeTS), 2016 | 21 | 2016 |
| R. State,“Improving missing data imputation with deep generative models,” RD Camino, CA Hammerschmidt arXiv preprint arXiv:1902.10666, 2019 | 19 | 2019 |
| Federated learning for cyber security: SOC collaboration for malicious URL detection E Khramtsova, C Hammerschmidt, S Lagraa, R State 2020 IEEE 40th International Conference on Distributed Computing Systems …, 2020 | 18 | 2020 |
| Behavioral clustering of non-stationary IP flow record data C Hammerschmidt, S Marchal, R State, S Verwer Network and Service Management (CNSM), 2016 12th International Conference on …, 2016 | 18* | 2016 |
| Interpreting Finite Automata for Sequential Data CA Hammerschmidt, S Verwer, Q Lin, R State arXiv preprint arXiv:1611.07100, 2016 | 15* | 2016 |
| Efficient Learning of Communication Profiles from IP Flow Records C Hammerschmidt, S Marchal, R State, G Pellegrino, S Verwer Local Computer Networks (LCN), 2016 IEEE 41st Conference on, 559-562, 2016 | 15 | 2016 |
| Beyond labeling: Using clustering to build network behavioral profiles of malware families A Nadeem, C Hammerschmidt, CH Gañán, S Verwer Malware analysis using artificial intelligence and deep learning, 381-409, 2021 | 12 | 2021 |
| Reliable Machine Learning for Networking: Key Issues and Approaches CA Hammerschmidt, S Garcia, S Verwer, R State Local Computer Networks (LCN), 2017 IEEE 42nd Conference on, 167-170, 2017 | 11 | 2017 |
| The robust malware detection challenge and greedy random accelerated multi-bit search S Verwer, A Nadeem, C Hammerschmidt, L Bliek, A Al-Dujaili, ... Proceedings of the 13th ACM Workshop on Artificial Intelligence and Security …, 2020 | 10 | 2020 |
| Working with deep generative models and tabular data imputation RD Camino, C Hammerschmidt First Workshop on the Art of Learning with Missing Values (Artemiss), 2020 | 7 | 2020 |
| An experimental analysis of fraud detection methods in enterprise telecommunication data using unsupervised outlier ensembles G Kaiafas, C Hammerschmidt, ... IEEE Symposium on Integrated Network and Service Management (IM), 37-42, 2019 | 6* | 2019 |
| Learning deterministic finite automata from infinite alphabets G Pellegrino, C Hammerschmidt, Q Lin, S Verwer International Conference on Grammatical Inference, 120-131, 2017 | 6 | 2017 |
| Flexfringe: Modeling software behavior by learning probabilistic automata S Verwer, C Hammerschmidt arXiv preprint arXiv:2203.16331, 2022 | 5 | 2022 |
| Oversampling tabular data with deep generative models: Is it worth the effort? RD Camino, CA Hammerschmidt PMLR, 2020 | 5 | 2020 |
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