| Reference : Are Your Training Datasets Yet Relevant? - An Investigation into the Importance of Ti... |
| Scientific congresses, symposiums and conference proceedings : Paper published in a book | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/20299 | |||
| Are Your Training Datasets Yet Relevant? - An Investigation into the Importance of Timeline in Machine Learning-Based Malware Detection | |
| English | |
Allix, Kevin  [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Bissyande, Tegawendé François D Assise [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Klein, Jacques [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Le Traon, Yves [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| 2015 | |
| Engineering Secure Software and Systems - 7th International Symposium ESSoS 2015, Milan, Italy, March 4-6, 2015. Proceedings | |
| Springer International Publishing | |
| 51-67 | |
| Yes | |
| 978-3-319-15617-0 | |
| 7th International Symposium on Engineering Secure Software and Systems, ESSoS'15 | |
| from 04-03-2015 to 06-03-2015 | |
| Milano | |
| Italy | |
| [en] Machine Learning ; Malware Detection ; Time ; Android | |
| [en] In this paper, we consider the relevance of timeline in the construction of datasets,
to highlight its impact on the performance of a machine learning-based malware detection scheme. Typically, we show that simply picking a random set of known malware to train a malware detector, as it is done in many assessment scenarios from the literature, yields significantly biased results. In the process of assessing the extent of this impact through various experiments, we were also able to con- firm a number of intuitive assumptions about Android malware. For instance, we discuss the existence of Android malware lineages and how they could impact the performance of malware detection in the wild. | |
| University of Luxembourg: High Performance Computing - ULHPC | |
| http://hdl.handle.net/10993/20299 | |
| 10.1007/978-3-319-15618-7_5 | |
| http://dx.doi.org/10.1007/978-3-319-15618-7_5 |
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