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Alexander Warnecke
Alexander Warnecke
Dirección de correo verificada de tu-berlin.de
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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
3292022
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
1182020
Machine unlearning of features and labels
A Warnecke, L Pirch, C Wressnegger, K Rieck
Network and Distributed System Security Symposium (NDSS), 2023
972023
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
252021
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
132021
Don’t paint it black: White-box explanations for deep learning in computer security
A Warnecke, D Arp, C Wressnegger, K Rieck
CoRR, 2019
102019
Manipulating feature visualizations with gradient slingshots
D Bareeva, MMC Höhne, A Warnecke, L Pirch, KR Müller, K Rieck, ...
arXiv preprint arXiv:2401.06122, 2024
32024
Convolutional neural networks for movement prediction in videos
A Warnecke, T Lüddecke, F Wörgötter
German Conference on Pattern Recognition, 215-225, 2017
32017
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
12023
Evil from Within: Machine Learning Backdoors through Hardware Trojans
A Warnecke, J Speith, JN Möller, K Rieck, C Paar
arXiv preprint arXiv:2304.08411, 2023
12023
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
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