| Dos and don’ts of machine learning in computer security D Arp, E Quiring, F Pendlebury, A Warnecke, F Pierazzi, C Wressnegger, ... 31st USENIX Security Symposium (USENIX Security 22), USENIX Association …, 2022 | 444 | 2022 |
| Machine unlearning of features and labels A Warnecke, L Pirch, C Wressnegger, K Rieck Network and Distributed System Security Symposium (NDSS), 2023 | 181 | 2023 |
| Evaluating explanation methods for deep learning in security A Warnecke, D Arp, C Wressnegger, K Rieck 2020 IEEE european symposium on security and privacy (EuroS&P), 158-174, 2020 | 153 | 2020 |
| Explaining graph neural networks for vulnerability discovery T Ganz, M Härterich, A Warnecke, K Rieck Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security …, 2021 | 31 | 2021 |
| Tagvet: Vetting malware tags using explainable machine learning L Pirch, A Warnecke, C Wressnegger, K Rieck Proceedings of the 14th European Workshop on Systems Security, 34-40, 2021 | 20 | 2021 |
| Don’t paint it black: White-box explanations for deep learning in computer security A Warnecke, D Arp, C Wressnegger, K Rieck CoRR, 2019 | 14 | 2019 |
| Lessons learned on machine learning for computer security D Arp, E Quiring, F Pendlebury, A Warnecke, F Pierazzi, C Wressnegger, ... IEEE Security & Privacy 21 (5), 72-77, 2023 | 5 | 2023 |
| Convolutional neural networks for movement prediction in videos A Warnecke, T Lüddecke, F Wörgötter German Conference on Pattern Recognition, 215-225, 2017 | 5 | 2017 |
| Manipulating feature visualizations with gradient slingshots D Bareeva, MMC Höhne, A Warnecke, L Pirch, KR Müller, K Rieck, ... arXiv preprint arXiv:2401.06122, 2024 | 3 | 2024 |
| Evil from Within: Machine Learning Backdoors Through Dormant Hardware Trojans A Warnecke, J Speith, JN Möller, K Rieck, C Paar | 3* | |
| Pitfalls in Machine Learning for Computer Security D Arp, E Quiring, F Pendlebury, A Warnecke, F Pierazzi, C Wressnegger, ... Communications of the ACM 67 (11), 104-112, 2024 | | 2024 |
| Security viewpoints on explainable machine learning A Warnecke | | 2024 |
| BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and Data: Schlussbericht DC Arp, E Quiring, A Warnecke, K Rieck Technische Universität Braunschweig, Institut für Systemsicherheit, 2021 | | 2021 |
| Abschlussbericht des Teilvorhabens: Verhaltensanalyse von Schadcode mit maschinellem Lernen im BMBF-Vorhaben: Effiziente Verhaltensanalyse von modernem Schadcode (VAMOS) K Rieck, A Warnecke, L Pirch Technische Universität Braunschweig, 2020 | | 2020 |
Konrad RieckTechnische Universität BerlinPatvirtintas el. paštas tu-berlin.de