| 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, 23-26, 2014 | 2572 | 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 | 927 | 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 | 877 | 2008 |
| 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 | 695 | 2014 |
| Measuring and detecting fast-flux service networks T Holz, C Gorecki, K Rieck, FC Freiling Proceedings of the Network & Distributed System Security Symposium, 2008 | 582* | 2008 |
| Toward supervised anomaly detection N Görnitz, M Kloft, K Rieck, U Brefeld Journal of Artificial Intelligence Research 46, 235-262, 2013 | 459 | 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 | 412 | 2013 |
| Learning intrusion detection: supervised or unsupervised? P Laskov, P Düssel, C Schäfer, K Rieck Image Analysis and Processing–ICIAP 2005: 13th International Conference …, 2005 | 363 | 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 | 313 | 2017 |
| Cujo: Efficient detection and prevention of drive-by-download attacks K Rieck, T Krueger, A Dewald | 283* | |
| Generalized vulnerability extrapolation using abstract syntax trees F Yamaguchi, M Lottmann, K Rieck Proceedings of the 28th annual computer security applications conference …, 2012 | 276 | 2012 |
| 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 | 253 | 2015 |
| 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 | 228 | 2015 |
| Vulnerability extrapolation: Assisted discovery of vulnerabilities using machine learning F Yamaguchi, K Rieck 5th USENIX Workshop on Offensive Technologies (WOOT 11), 2011 | 211 | 2011 |
| 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 | 208 | 2013 |
| 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 | 206 | 2022 |
| Poisoning behavioral malware clustering B Biggio, K Rieck, D Ariu, C Wressnegger, I Corona, G Giacinto, F Roli Proceedings of the 2014 workshop on artificial intelligent and security …, 2014 | 161 | 2014 |
| Continuous authentication on mobile devices by analysis of typing motion behavior H Gascon, S Uellenbeck, C Wolf, K Rieck Sicherheit 2014–Sicherheit, Schutz und Zuverlässigkeit, 2014 | 157 | 2014 |
| 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 | 145 | 2015 |
| Linear-Time Computation of Similarity Measures for Sequential Data. K Rieck, P Laskov Journal of Machine Learning Research 9 (1), 2008 | 144 | 2008 |
Fabian YamaguchiWhirly Labs (Pty) Ltd, Qwiet.AI, Stellenbosch UniversityVerified email at codeminers.org
