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Daniel Arp
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- affiliation (PhD 2019): Braunschweig University of Technology, Germany
- affiliation: University of Göttingen, Germany
- affiliation: TU Berlin, Germany
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2020 – today
- 2024
- [j4]Daniel Arp
, Erwin Quiring
Pitfalls in Machine Learning for Computer Security. Commun. ACM 67(11): 104-112 (2024) - [c21]Moritz Pfister, Robert Michael, Max Boll, Cosima Körfer, Konrad Rieck, Daniel Arp:
Listening Between the Bits: Privacy Leaks in Audio Fingerprints. DIMVA 2024: 184-204 - [i7]Zeliang Kan, Shae McFadden, Daniel Arp, Feargus Pendlebury, Roberto Jordaney, Johannes Kinder, Fabio Pierazzi
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version). CoRR abs/2402.01359 (2024) - 2023
- [j3]Daniel Arp
Lessons Learned on Machine Learning for Computer Security. IEEE Secur. Priv. 21(5): 72-77 (2023) - [c20]Theo Chow
Drift Forensics of Malware Classifiers. AISec@CCS 2023: 197-207 - 2022
- [c19]Stefan Czybik, Daniel Arp, Konrad Rieck:
Quantifying the Risk of Wormhole Attacks on Bluetooth Contact Tracing. CODASPY 2022: 264-275 - [c18]Vera Wesselkamp, Konrad Rieck, Daniel Arp, Erwin Quiring:
Misleading Deep-Fake Detection with GAN Fingerprints. SP (Workshops) 2022: 59-65 - [c17]Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, Konrad Rieck:
Dos and Don'ts of Machine Learning in Computer Security. USENIX Security Symposium 2022: 3971-3988 - [e1]Christian Wressnegger, Delphine Reinhardt, Thomas Barber, Bernhard C. Witt, Daniel Arp, Zoltán Ádám Mann:
Sicherheit, Schutz und Zuverlässigkeit: Konferenzband der 11. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik e.V. (GI), Sicherheit 2022, Karlsruhe, Germany, April 5-8, 2022. LNI P-323, Gesellschaft für Informatik e.V. 2022, ISBN 978-3-88579-717-3 [contents] - [i6]Vera Wesselkamp, Konrad Rieck, Daniel Arp, Erwin Quiring:
Misleading Deep-Fake Detection with GAN Fingerprints. CoRR abs/2205.12543 (2022) - 2021
- [c16]Jan Malte Hilgefort
Spying through Virtual Backgrounds of Video Calls. AISec@CCS 2021: 135-144 - 2020
- [c15]Alexander Warnecke, Daniel Arp, Christian Wressnegger, Konrad Rieck:
Evaluating Explanation Methods for Deep Learning in Security. EuroS&P 2020: 158-174 - [c14]Erwin Quiring, David Klein, Daniel Arp, Martin Johns, Konrad Rieck:
Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning. USENIX Security Symposium 2020: 1363-1380 - [i5]Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, Konrad Rieck:
Dos and Don'ts of Machine Learning in Computer Security. CoRR abs/2010.09470 (2020) - [i4]Erwin Quiring, Lukas Pirch, Michael Reimsbach, Daniel Arp, Konrad Rieck:
Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification. CoRR abs/2010.09569 (2020)
2010 – 2019
- 2019
- [b1]Daniel Arp:
Efficient and Explainable Detection of Mobile Malware with Machine Learning. Braunschweig University of Technology, Germany, 2019 - [j2]Ambra Demontis
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection. IEEE Trans. Dependable Secur. Comput. 16(4): 711-724 (2019) - [p1]Daniel Arp:
Erkennung mobiler Schadsoftware mit maschinellen Lernverfahren. Ausgezeichnete Informatikdissertationen 2019: 9-18 - [i3]Alexander Warnecke, Daniel Arp, Christian Wressnegger, Konrad Rieck:
Don't Paint It Black: White-Box Explanations for Deep Learning in Computer Security. CoRR abs/1906.02108 (2019) - 2018
- [c13]Erwin Quiring, Daniel Arp, Konrad Rieck:
Forgotten Siblings: Unifying Attacks on Machine Learning and Digital Watermarking. EuroS&P 2018: 488-502 - [c12]Daniel Arp, Erwin Quiring, Tammo Krueger, Stanimir Dragiev, Konrad Rieck:
Privacy-Enhanced Fraud Detection with Bloom Filters. SecureComm (1) 2018: 396-415 - 2017
- [c11]Hugo Gascon, Bernd Grobauer
Mining Attributed Graphs for Threat Intelligence. CODASPY 2017: 15-22 - [c10]Daniel Arp, Erwin Quiring, Christian Wressnegger, Konrad Rieck:
Privacy Threats through Ultrasonic Side Channels on Mobile Devices. EuroS&P 2017: 35-47 - [i2]Erwin Quiring, Daniel Arp, Konrad Rieck:
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking. CoRR abs/1703.05561 (2017) - [i1]Ambra Demontis, Marco Melis, Battista Biggio, Davide Maiorca, Daniel Arp, Konrad Rieck, Igino Corona, Giorgio Giacinto, Fabio Roli:
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection. CoRR abs/1704.08996 (2017) - 2016
- [c9]Christian Wressnegger, Fabian Yamaguchi, Daniel Arp, Konrad Rieck:
Comprehensive Analysis and Detection of Flash-Based Malware. DIMVA 2016: 101-121 - 2015
- [j1]Michael Spreitzenbarth, Thomas Schreck
Mobile-Sandbox: combining static and dynamic analysis with machine-learning techniques. Int. J. Inf. Sec. 14(2): 141-153 (2015) - [c8]Henning Perl, Sergej Dechand, Matthew Smith
VCCFinder: Finding Potential Vulnerabilities in Open-Source Projects to Assist Code Audits. CCS 2015: 426-437 - [c7]Daniel Arp, Fabian Yamaguchi, Konrad Rieck:
Torben: A Practical Side-Channel Attack for Deanonymizing Tor Communication. AsiaCCS 2015: 597-602 - [c6]Hugo Gascon, Christian Wressnegger, Fabian Yamaguchi, Daniel Arp, Konrad Rieck:
Pulsar: Stateful Black-Box Fuzzing of Proprietary Network Protocols. SecureComm 2015: 330-347 - 2014
- [c5]Daniel Arp, Michael Spreitzenbarth, Malte Hubner, Hugo Gascon, Konrad Rieck:
DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket. NDSS 2014 - [c4]Fabian Yamaguchi, Nico Golde, Daniel Arp, Konrad Rieck:
Modeling and Discovering Vulnerabilities with Code Property Graphs. IEEE Symposium on Security and Privacy 2014: 590-604 - 2013
- [c3]Hugo Gascon, Fabian Yamaguchi, Daniel Arp, Konrad Rieck:
Structural detection of android malware using embedded call graphs. AISec 2013: 45-54 - [c2]Christian Wressnegger, Guido Schwenk, Daniel Arp, Konrad Rieck:
A close look on n-grams in intrusion detection: anomaly detection vs. classification. AISec 2013: 67-76 - 2012
- [c1]Volker Eiselein, Daniel Arp, Michael Pätzold, Thomas Sikora:
Real-Time Multi-human Tracking Using a Probability Hypothesis Density Filter and Multiple Detectors. AVSS 2012: 325-330
Coauthor Index
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last updated on 2024-11-07 20:36 CET by the dblp team
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