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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Abraham, A. Andriatsimandefitra, R. Brunelat, A. Lalande, J.-F. Viet Triem Tong, V. |
| Copyright Year | 2015 |
| Description | Author affiliation: INSA Centre Val de Loire, Univ. Orleans LIFO EA 4022, F-18020 Bourges, France (Lalande, J.-F.) || y ENS Cachan F-94230 Cachan, France (Abraham, A.) || EPI CIDRE, CentraleSupelec, Inria, Universite de Rennes 1, CNRS IRISA UMR 6074, F-35065 Rennes, France (Andriatsimandefitra, R.; Brunelat, A.; Viet Triem Tong, V.) |
| Abstract | Android malware authors use sophisticated techniques to hide the malicious intent of their applications. They use cryptography or obfuscation techniques to avoid detection during static analysis. They can also avoid detection during a dynamic analysis. Frequently, the malicious execution is postponed as long as the malware is not convinced that it is running in a real smartphone of a real user. However, we believe that dynamic analysis methods give good results when they really monitor the malware execution. In this article1, we propose a method to enhance the execution of the malicious code of unknown malware. We especially target malware that have triggering protections, for example branching conditions that wait for an event or expect a specific value for a variable before triggering malicious execution. In these cases, solely executing the malware is far from being sufficient. We propose to force the triggering of the malicious code by combining two contributions. First, we define an algorithm that automatically identifies potentially malicious code. Second, we propose an enhanced monkey called GroddDroid, that stimulates the GUI of an application and forces the execution of some branching conditions if needed. The forcing is used by GroddDroid to push the execution flow towards the previously identified malicious parts of the malware and execute it. The source code for our experiments with GroddDroid is released as free software2. We have verified on a malware dataset that we investigated manually that the malicious code is accurately executed by GroddDroid. Additionally, on a large dataset of 100 malware we precisely identify the nature of the suspicious code and we succeed to execute it at 28%. |
| Starting Page | 119 |
| Ending Page | 127 |
| File Size | 775283 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781509003174 |
| e-ISBN | 9781509003198 |
| DOI | 10.1109/MALWARE.2015.7413692 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-10-20 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Java Force Humanoid robots Malware Androids Monitoring Graphical user interfaces |
| Content Type | Text |
| Resource Type | Article |
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