Real-Time Multi-human Tracking Using a Probability Hypothesis Density Filter and Multiple Detectors. - Volker Eiselein; Daniel Arp; Michael Pätzold; Thomas Sikora (AVSS, 2012)

Structural detection of android malware using embedded call graphs. - Hugo Gascon; Fabian Yamaguchi; Daniel Arp; Konrad Rieck (AISec, 2013)

A close look on n-grams in intrusion detection: anomaly detection vs. classification. - Christian Wressnegger; Guido Schwenk; Daniel Arp; Konrad Rieck (AISec, 2013)

DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket. - Daniel Arp; Michael Spreitzenbarth; Malte Hubner; Hugo Gascon; Konrad Rieck (NDSS, 2014)

Modeling and Discovering Vulnerabilities with Code Property Graphs. - Fabian Yamaguchi; Nico Golde; Daniel Arp; Konrad Rieck (IEEE Symposium on Security and Privacy, 2014)

Torben: A Practical Side-Channel Attack for Deanonymizing Tor Communication. - Daniel Arp; Fabian Yamaguchi; Konrad Rieck (AsiaCCS, 2015)

VCCFinder: Finding Potential Vulnerabilities in Open-Source Projects to Assist Code Audits. - Henning Perl; Sergej Dechand; Matthew Smith 001; Daniel Arp; Fabian Yamaguchi; Konrad Rieck; Sascha Fahl; Yasemin Acar (CCS, 2015)

Pulsar: Stateful Black-Box Fuzzing of Proprietary Network Protocols. - Hugo Gascon; Christian Wressnegger; Fabian Yamaguchi; Daniel Arp; Konrad Rieck (SecureComm, 2015)

Mobile-Sandbox: combining static and dynamic analysis with machine-learning techniques. - Michael Spreitzenbarth; Thomas Schreck; Florian Echtler; Daniel Arp; Johannes Hoffmann (Int. J. Inf. Sec., 2015)

Comprehensive Analysis and Detection of Flash-Based Malware. - Christian Wressnegger; Fabian Yamaguchi; Daniel Arp; Konrad Rieck (DIMVA, 2016)

Mining Attributed Graphs for Threat Intelligence. - Hugo Gascon; Bernd Grobauer; Thomas Schreck; Lukas Rist; Daniel Arp; Konrad Rieck (CODASPY, 2017)

Privacy Threats through Ultrasonic Side Channels on Mobile Devices. - Daniel Arp; Erwin Quiring; Christian Wressnegger; Konrad Rieck (EuroS&P, 2017)

Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking. - Erwin Quiring; Daniel Arp; Konrad Rieck (CoRR, 2017)

Forgotten Siblings: Unifying Attacks on Machine Learning and Digital Watermarking. - Erwin Quiring; Daniel Arp; Konrad Rieck (EuroS&P, 2018)

Privacy-Enhanced Fraud Detection with Bloom Filters. - Daniel Arp; Erwin Quiring; Tammo Krueger; Stanimir Dragiev; Konrad Rieck (SecureComm (1), 2018)

Don't Paint It Black: White-Box Explanations for Deep Learning in Computer Security. - Alexander Warnecke; Daniel Arp; Christian Wressnegger; Konrad Rieck (CoRR, 2019)

Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection. - Ambra Demontis; Marco Melis; Battista Biggio; Davide Maiorca; Daniel Arp; Konrad Rieck; Igino Corona; Giorgio Giacinto; Fabio Roli (IEEE Trans. Dependable Secur. Comput., 2019)

Efficient and Explainable Detection of Mobile Malware with Machine Learning. - Daniel Arp (2019)

Erkennung mobiler Schadsoftware mit maschinellen Lernverfahren. - Daniel Arp (Ausgezeichnete Informatikdissertationen, 2019)

Evaluating Explanation Methods for Deep Learning in Security. - Alexander Warnecke; Daniel Arp; Christian Wressnegger; Konrad Rieck (EuroS&P, 2020)

Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning. - Erwin Quiring; David Klein 001; Daniel Arp; Martin Johns; Konrad Rieck (USENIX Security Symposium, 2020)

Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification. - Erwin Quiring; Lukas Pirch; Michael Reimsbach; Daniel Arp; Konrad Rieck (CoRR, 2020)

Spying through Virtual Backgrounds of Video Calls. - Jan Malte Hilgefort; Daniel Arp; Konrad Rieck (AISec@CCS, 2021)

Quantifying the Risk of Wormhole Attacks on Bluetooth Contact Tracing. - Stefan Czybik; Daniel Arp; Konrad Rieck (CODASPY, 2022)

Misleading Deep-Fake Detection with GAN Fingerprints. - Vera Wesselkamp; Konrad Rieck; Daniel Arp; Erwin Quiring (SP (Workshops), 2022)

Dos and Don'ts of Machine Learning in Computer Security. - Daniel Arp; Erwin Quiring; Feargus Pendlebury; Alexander Warnecke; Fabio Pierazzi; Christian Wressnegger; Lorenzo Cavallaro; Konrad Rieck (USENIX Security Symposium, 2022)

Drift Forensics of Malware Classifiers. - Theo Chow; Zeliang Kan; Lorenz Linhardt; Lorenzo Cavallaro; Daniel Arp; Fabio Pierazzi (AISec@CCS, 2023)

Lessons Learned on Machine Learning for Computer Security. - Daniel Arp; Erwin Quiring; Feargus Pendlebury; Alexander Warnecke; Fabio Pierazzi; Christian Wressnegger; Lorenzo Cavallaro; Konrad Rieck (IEEE Secur. Priv., 2023)

Listening Between the Bits: Privacy Leaks in Audio Fingerprints. - Moritz Pfister; Robert Michael; Max Boll; Cosima Körfer; Konrad Rieck; Daniel Arp (DIMVA, 2024)

TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version). - Zeliang Kan; Shae McFadden; Daniel Arp; Feargus Pendlebury; Roberto Jordaney; Johannes Kinder; Fabio Pierazzi; Lorenzo Cavallaro (CoRR, 2024)

On the Effectiveness of Adversarial Training on Malware Classifiers. - Hamid Bostani; Jacopo Cortellazzi; Daniel Arp; Fabio Pierazzi; Veelasha Moonsamy; Lorenzo Cavallaro (CoRR, 2024)

Pitfalls in Machine Learning for Computer Security. - Daniel Arp; Erwin Quiring; Feargus Pendlebury; Alexander Warnecke; Fabio Pierazzi; Christian Wressnegger; Lorenzo Cavallaro; Konrad Rieck (Commun. ACM, 2024)

Breaking Out from the TESSERACT: Reassessing ML-based Malware Detection under Spatio-Temporal Drift. - Theo Chow; Mario D'Onghia; Lorenz Linhardt; Zeliang Kan; Daniel Arp; Lorenzo Cavallaro; Fabio Pierazzi (CoRR, 2025)