| Drebin: Effective and explainable detection of android malware in your pocket. D Arp, M Spreitzenbarth, M Hubner, H Gascon, K Rieck, C Siemens Ndss 14 (1), 23-26, 2014 | 3297 | 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 | 1158 | 2014 |
| Automatic analysis of malware behavior using machine learning K Rieck, P Trinius, C Willems, T Holz Journal of computer security 19 (4), 639-668, 2011 | 1051 | 2011 |
| Learning and classification of malware behavior K Rieck, T Holz, C Willems, P Düssel, P Laskov Detection of Intrusions and Malware, and Vulnerability Assessment, 108-125, 2008 | 959 | 2008 |
| 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), 3971-3988, 2022 | 627 | 2022 |
| Measuring and detecting fast-flux service networks T Holz, C Gorecki, K Rieck, FC Freiling Proceedings of the Network & Distributed System Security Symposium, 2008 | 611* | 2008 |
| Toward supervised anomaly detection N Görnitz, M Kloft, K Rieck, U Brefeld Journal of Artificial Intelligence Research 46, 235-262, 2013 | 607 | 2013 |
| 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 | 460 | 2013 |
| Learning intrusion detection: supervised or unsupervised? P Laskov, P Düssel, C Schäfer, K Rieck International Conference on Image Analysis and Processing, 50-57, 2005 | 437 | 2005 |
| 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 | 390 | 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 | 364 | 2015 |
| Generalized vulnerability extrapolation using abstract syntax trees F Yamaguchi, M Lottmann, K Rieck Proceedings of the 28th annual computer security applications conference …, 2012 | 340 | 2012 |
| Machine unlearning of features and labels A Warnecke, L Pirch, C Wressnegger, K Rieck arXiv preprint arXiv:2108.11577, 2021 | 316 | 2021 |
| Cujo: Efficient detection and prevention of drive-by-download attacks K Rieck, T Krueger, A Dewald | 293* | |
| Automatic inference of search patterns for taint-style vulnerabilities F Yamaguchi, A Maier, H Gascon, K Rieck 2015 IEEE symposium on security and privacy, 797-812, 2015 | 278 | 2015 |
| Chucky: Exposing missing checks in source code for vulnerability discovery F Yamaguchi, C Wressnegger, H Gascon, K Rieck Proceedings of the 2013 ACM SIGSAC conference on Computer & communications …, 2013 | 258 | 2013 |
| Vulnerability extrapolation: Assisted discovery of vulnerabilities using machine learning F Yamaguchi, K Rieck 5th USENIX workshop on offensive technologies (WOOT 11), 2011 | 250 | 2011 |
| 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 | 203 | 2020 |
| 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 | 202 | 2015 |
| When coding style survives compilation: De-anonymizing programmers from executable binaries A Caliskan, F Yamaguchi, E Dauber, R Harang, K Rieck, R Greenstadt, ... arXiv preprint arXiv:1512.08546, 2015 | 180 | 2015 |
Daniel ArpTechnische Universität WienVerified email at tuwien.ac.at
