| Reference : Machine Learning-Based Malware Detection for Android Applications: History Matters! |
| Reports : Other | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/17251 | |||
| Machine Learning-Based Malware Detection for Android Applications: History Matters! | |
| English | |
Allix, Kevin  [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > > ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >] | |
| 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) >] | |
| Le Traon, Yves [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >] | |
| 26-May-2014 | |
| University of Luxembourg, SnT | |
| 17 | |
| 978-2-87971-132-4 | |
| Luxembourg | |
| Luxembourg | |
| [en] Machine Learning-based malware detection is a promis-
ing scalable method for identifying suspicious applica- tions. In particular, in today’s mobile computing realm where thousands of applications are daily poured into markets, such a technique could be valuable to guaran- tee a strong filtering of malicious apps. The success of machine-learning approaches however is highly de- pendent on (1) the quality of the datasets that are used for training and of (2) the appropriateness of the tested datasets with regards to the built classifiers. Unfortu- nately, there is scarce mention of these aspects in the evaluation of existing state-of-the-art approaches in the literature. In this paper, we consider the relevance of history in the construction of datasets, to highlight its impact on the performance of the malware detection scheme. Typ- ically, we show that simply picking a random set of known malware to train a malware detector, as it is done in most 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 confirm a number of intuitive assump- tions 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 | |
| Researchers | |
| http://hdl.handle.net/10993/17251 |
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