Deep android malware detection
| dc.cclicence | CC-BY-NC | en |
| dc.contributor.author | McLaughlin, Niall | en |
| dc.contributor.author | Martinez del Rincon, Jesus | en |
| dc.contributor.author | Kang, BooJoong | en |
| dc.contributor.author | Yerima, Suleiman | en |
| dc.contributor.author | Miller, Paul | en |
| dc.contributor.author | Sezer, Sakir | en |
| dc.contributor.author | Safaei, Yeganeh | en |
| dc.contributor.author | Trickel, Erik | en |
| dc.contributor.author | Zhao, Ziming | en |
| dc.contributor.author | Doupe, Adam | en |
| dc.contributor.author | Gail Joon Ahn | en |
| dc.date.acceptance | 2017 | en |
| dc.date.accessioned | 2018-10-31T13:33:22Z | |
| dc.date.available | 2018-10-31T13:33:22Z | |
| dc.date.issued | 2017-03 | |
| dc.description.abstract | In this paper, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN). Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. Features indicative of malware are automatically learned by the network from the raw opcode sequence thus removing the need for hand-engineered malware features. The training pipeline of our proposed system is much simpler than existing n-gram based malware detection methods, as the network is trained end-to-end to jointly learn appropriate features and to perform classification, thus removing the need to explicitly enumerate millions of n-grams during training. The network design also allows the use of long n-gram like features, not computationally feasible with existing methods. Once trained, the network can be efficiently executed on a GPU, allowing a very large number of files to be scanned quickly. | en |
| dc.funder | N/A | en |
| dc.identifier.citation | McLaughlin, N., Martinez del Rincon, J., Kang, B., Yerima, S., Miller, P., Sezer, S., ... Joon Ahn, G. (2017). Deep Android Malware Detection. In Proceedings of the ACM Conference on Data and Applications Security and Privacy (CODASPY) 2017 Association for Computing Machinery (ACM). | en |
| dc.identifier.doi | https://doi.org/10.1145/3029806.3029823 | |
| dc.identifier.isbn | 978-1-4503-4523-1 | |
| dc.identifier.uri | http://hdl.handle.net/2086/16947 | |
| dc.language.iso | en | en |
| dc.peerreviewed | Yes | en |
| dc.projectid | N//a | en |
| dc.publisher | ACM | en |
| dc.researchinstitute | Cyber Technology Institute (CTI) | en |
| dc.subject | machine learning | en |
| dc.subject | neural networks | en |
| dc.subject | convolutional neural networks | en |
| dc.subject | android malware | en |
| dc.subject | malware detection | en |
| dc.subject | opcodes | en |
| dc.subject | n-grams | en |
| dc.title | Deep android malware detection | en |
| dc.type | Conference | en |