| Title | Scalable Function Call Graph-based Malware Classification |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Hassen, Mehadi, Chan, Philip K. |
| Conference Name | Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4523-1 |
| Keywords | graph classification, Human Behavior, malware classification, Metrics, privacy, pubcrawl, resilience, Resiliency |
| Abstract | In an attempt to preserve the structural information in malware binaries during feature extraction, function call graph-based features have been used in various research works in malware classification. However, the approach usually employed when performing classification on these graphs, is based on computing graph similarity using computationally intensive techniques. Due to this, much of the previous work in this area incurred large performance overhead and does not scale well. In this paper, we propose a linear time function call graph (FCG) vector representation based on function clustering that has significant performance gains in addition to improved classification accuracy. We also show how this representation can enable using graph features together with other non-graph features. |
| URL | http://doi.acm.org/10.1145/3029806.3029824 |
| DOI | 10.1145/3029806.3029824 |
| Citation Key | hassen_scalable_2017 |
Scalable Function Call Graph-based Malware Classification