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Konstantin Böttinger
Konstantin Böttinger
Bestätigte E-Mail-Adresse bei aisec.fraunhofer.de
Titel
Zitiert von
Zitiert von
Jahr
Stack overflow considered harmful? the impact of copy&paste on android application security
F Fischer, K Böttinger, H Xiao, C Stransky, Y Acar, M Backes, S Fahl
2017 IEEE symposium on security and privacy (SP), 121-136, 2017
3572017
Deep reinforcement fuzzing
K Böttinger, P Godefroid, R Singh
2018 IEEE Security and Privacy Workshops (SPW), 116-122, 2018
1272018
High-performance unsupervised anomaly detection for cyber-physical system networks
P Schneider, K Böttinger
Proceedings of the 2018 workshop on cyber-physical systems security and …, 2018
932018
Does audio deepfake detection generalize?
NM Müller, P Czempin, F Dieckmann, A Froghyar, K Böttinger
arXiv preprint arXiv:2203.16263, 2022
902022
A collaborative cyber incident management system for European interconnected critical infrastructures
G Settanni, F Skopik, Y Shovgenya, R Fiedler, M Carolan, D Conroy, ...
Journal of Information Security and Applications 34, 166-182, 2017
702017
Speech is silver, silence is golden: What do ASVspoof-trained models really learn?
NM Müller, F Dieckmann, P Czempin, R Canals, K Böttinger, J Williams
arXiv preprint arXiv:2106.12914, 2021
572021
Human perception of audio deepfakes
NM Müller, K Pizzi, J Williams
Proceedings of the 1st International Workshop on Deepfake Detection for …, 2022
522022
DLA: dense-layer-analysis for adversarial example detection
P Sperl, CY Kao, P Chen, X Lei, K Böttinger
2020 IEEE European Symposium on Security and Privacy (EuroS&P), 198-215, 2020
492020
Stack Overflow Considered Helpful! Deep Learning Security Nudges Towards Stronger Cryptography
F Fischer, H Xiao, CY Kao, Y Stachelscheid, B Johnson, D Raza, ...
442019
Deepfuzz: Triggering vulnerabilities deeply hidden in binaries
K Böttinger, C Eckert
Detection of Intrusions and Malware, and Vulnerability Assessment: 13th …, 2016
412016
Deutsche Normungsroadmap Künstliche Intelligenz
R Adler, A Bunte, S Burton, J Großmann, A Jaschke, P Kleen, JM Lorenz, ...
DIN, 2022
262022
Data poisoning attacks on regression learning and corresponding defenses
N Müller, D Kowatsch, K Böttinger
2020 IEEE 25th Pacific Rim International Symposium on Dependable Computing …, 2020
212020
Gradient masking and the underestimated robustness threats of differential privacy in deep learning
F Boenisch, P Sperl, K Böttinger
arXiv preprint arXiv:2105.07985, 2021
202021
On GDPR Compliance of Companies’ Privacy Policies
K Böttinger
Text, Speech, and Dialogue: 22nd International Conference, TSD 2019 …, 2019
18*2019
A unified architecture for industrial IoT security requirements in open platform communications
G Hansch, P Schneider, K Fischer, K Böttinger
2019 24th ieee international conference on emerging technologies and factory …, 2019
172019
Distributed Anomaly Detection of Single Mote Attacks in RPL Networks.
NM Müller, P Debus, D Kowatsch, K Böttinger
ICETE (2), 378-385, 2019
172019
Activation anomaly analysis
P Sperl, JP Schulze, K Böttinger
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2020
162020
Detecting fingerprinted data in TLS traffic
K Böttinger, D Schuster, C Eckert
Proceedings of the 10th ACM Symposium on Information, Computer and …, 2015
122015
Side-channel aware fuzzing
P Sperl, K Böttinger
Computer Security–ESORICS 2019: 24th European Symposium on Research in …, 2019
112019
Deep reinforcement fuzzing. In 2018 IEEE Security and Privacy Workshops (SPW), 116–122
K Böttinger, P Godefroid, R Singh
IEEE, San Francisco, CA, USA: IEEE, 2018
92018
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