Code clone detection in obfuscated Android apps
| dc.contributor.advisor | Stakhanova, Natalia | |
| dc.contributor.committeeMember | Keil, Mark | |
| dc.contributor.committeeMember | Roy, Banani | |
| dc.contributor.committeeMember | Bedeer Mohamed, Ebrahim | |
| dc.creator | Foroughipour, Ardalan | |
| dc.creator.orcid | 0000-0002-1832-448X | |
| dc.date.accessioned | 2022-01-28T14:03:51Z | |
| dc.date.available | 2022-01-28T14:03:51Z | |
| dc.date.created | 2021-11 | |
| dc.date.issued | 2022-01-28 | |
| dc.date.submitted | November 2021 | |
| dc.date.updated | 2022-01-28T14:03:51Z | |
| dc.description.abstract | The Android operating system has long become one of the main global smartphone operating systems. Both developers and malware authors often reuse code to expedite the process of creating new apps and malware samples. Code cloning is the most common way of reusing code in the process of developing Android apps. Finding code clones through the analysis of Android binary code is a challenging task that becomes more sophisticated when instances of code reuse are non-contiguous, reordered, or intertwined with other code. We introduce an approach for detecting cloned methods as well as small and non-contiguous code clones in obfuscated Android applications by simulating the execution of Android apps and then analyzing the subsequent execution traces. We first validate our approach’s ability on finding different types of code clones on 20 injected clones. Next we validate the resistance of our approach against obfuscation by comparing its results on a set of 1085 apps before and after code obfuscation. We obtain 78-87% similarity between the finding from non-obfuscated applications and four sets of obfuscated applications. We also investigated the presence of code clones among 1603 Android applications. We were able to find 44,776 code clones where 34% of code clones were seen from different applications and the rest are among different versions of an application. We also performed a comparative analysis between the clones found by our approach and the clones detected by Nicad on the source code of applications. Finally, we show a practical application of our approach for detecting variants of Android banking malware. Among 60,057 code clone clusters that are found among a dataset of banking malware, 92.9% of them were unique to one malware family or benign applications. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/10388/13796 | |
| dc.subject | Code simulation | |
| dc.subject | Code clone | |
| dc.subject | Code Obfuscation | |
| dc.subject | Android | |
| dc.subject | Malware | |
| dc.subject | Smali Code | |
| dc.title | Code clone detection in obfuscated Android apps | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Computer Science | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Saskatchewan | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.Sc.) |