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Malware Analysis on Android Using Supervised Machine Learning Techniques
| Content Provider | Semantic Scholar |
|---|---|
| Author | Rana, Shohel Sung, Andrew H. |
| Copyright Year | 2018 |
| Abstract | In recent years, a widespread research is conducted with the growth of malware resulted in the domain of malware analysis and detection in Android devices. Android, a mobile-based operating system currently having more than one billion active users with a high market impact that have inspired the expansion of malware by cyber criminals. Android implements a different architecture and security controls to solve the problems caused by malware, such as unique user ID (UID) for each application, system permissions, and its distribution platform Google Play. There are numerous ways to violate that fortification, and how the complexity of creating a new solution is enlarged while cybercriminals progress their skills to develop malware. A community including developer and researcher has been evolving substitutes aimed at refining the level of safety where numerous machine learning algorithms already been proposed or applied to classify or cluster malware including analysis techniques, frameworks, sandboxes, and systems security. One of the most promising techniques is the implementation of artificial intelligence solutions for malware analysis. In this paper, we evaluate numerous supervised machine learning algorithms by implementing a static analysis framework to make predictions for detecting malware on Android. |
| Starting Page | 178 |
| Ending Page | 188 |
| Page Count | 11 |
| File Format | PDF HTM / HTML |
| DOI | 10.17706/ijcce.2018.7.4.178-188 |
| Volume Number | 7 |
| Alternate Webpage(s) | https://aquila.usm.edu/cgi/viewcontent.cgi?article=16971&context=fac_pubs |
| Alternate Webpage(s) | http://www.ijcce.org/vol7/511-C0015.pdf |
| Alternate Webpage(s) | https://doi.org/10.17706/ijcce.2018.7.4.178-188 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
