Drebin: Effective and explainable detection of android malware in your pocket. D Arp, M Spreitzenbarth, M Hübner, H Gascon, K Rieck Ndss 14, 23-26, 2014 | 2053 | 2014 |
Modeling and discovering vulnerabilities with code property graphs F Yamaguchi, N Golde, D Arp, K Rieck 2014 IEEE Symposium on Security and Privacy, 590-604, 2014 | 410 | 2014 |
Structural detection of android malware using embedded call graphs H Gascon, F Yamaguchi, D Arp, K Rieck Proceedings of the 2013 ACM workshop on Artificial intelligence and security …, 2013 | 363 | 2013 |
Yes, machine learning can be more secure! a case study on android malware detection A Demontis, M Melis, B Biggio, D Maiorca, D Arp, K Rieck, I Corona, ... IEEE Transactions on Dependable and Secure Computing 16 (4), 711-724, 2017 | 203 | 2017 |
Vccfinder: Finding potential vulnerabilities in open-source projects to assist code audits H Perl, S Dechand, M Smith, D Arp, F Yamaguchi, K Rieck, S Fahl, Y Acar Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications …, 2015 | 175 | 2015 |
A close look on n-grams in intrusion detection: anomaly detection vs. classification C Wressnegger, G Schwenk, D Arp, K Rieck Proceedings of the 2013 ACM workshop on Artificial intelligence and security …, 2013 | 114 | 2013 |
Mobile-Sandbox: combining static and dynamic analysis with machine-learning techniques M Spreitzenbarth, T Schreck, F Echtler, D Arp, J Hoffmann International Journal of Information Security 14 (2), 141-153, 2015 | 111 | 2015 |
Pulsar: Stateful Black-Box Fuzzing of Proprietary Network Protocols H Gascon, C Wressnegger, F Yamaguchi, D Arp, K Rieck International Conference on Security and Privacy in Communication Systems …, 2015 | 83 | 2015 |
Real-time multi-human tracking using a probability hypothesis density filter and multiple detectors V Eiselein, D Arp, M Pätzold, T Sikora 2012 IEEE Ninth international conference on advanced video and signal-based …, 2012 | 75 | 2012 |
Privacy threats through ultrasonic side channels on mobile devices D Arp, E Quiring, C Wressnegger, K Rieck 2017 IEEE European Symposium on Security and Privacy (EuroS&P), 35-47, 2017 | 66 | 2017 |
Forgotten siblings: Unifying attacks on machine learning and digital watermarking E Quiring, D Arp, K Rieck 2018 IEEE European symposium on security and privacy (EuroS&P), 488-502, 2018 | 56 | 2018 |
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 | 34 | 2020 |
Adversarial Preprocessing: Understanding and Preventing {Image-Scaling} Attacks in Machine Learning E Quiring, D Klein, D Arp, M Johns, K Rieck 29th USENIX Security Symposium (USENIX Security 20), 1363-1380, 2020 | 29 | 2020 |
Torben: A practical side-channel attack for deanonymizing tor communication D Arp, F Yamaguchi, K Rieck Proceedings of the 10th ACM Symposium on Information, Computer and …, 2015 | 29 | 2015 |
Mining attributed graphs for threat intelligence H Gascon, B Grobauer, T Schreck, L Rist, D Arp, K Rieck Proceedings of the Seventh ACM on Conference on Data and Application …, 2017 | 27 | 2017 |
Comprehensive analysis and detection of flash-based malware C Wressnegger, F Yamaguchi, D Arp, K Rieck International Conference on Detection of Intrusions and Malware, and …, 2016 | 27 | 2016 |
Dos and don’ts of machine learning in computer security D Arp, E Quiring, F Pendlebury, A Warnecke, F Pierazzi, C Wressnegger, ... Proc. of the USENIX Security Symposium, 2022 | 26 | 2022 |
Analyzing and detecting Flash-based malware using lightweight multi-path exploration C Wressnegger, F Yamaguchi, D Arp, K Rieck University of Göttingen, Germany, 2015 | 7 | 2015 |
Don’t paint it black: White-box explanations for deep learning in computer security A Warnecke, D Arp, C Wressnegger, K Rieck CoRR, 2019 | 6 | 2019 |
Fraternal twins: Unifying attacks on machine learning and digital watermarking E Quiring, D Arp, K Rieck arXiv preprint arXiv:1703.05561, 2017 | 6 | 2017 |