Learning from Big Malwares
| Title | Learning from Big Malwares |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Song, Linhai, Huang, Heqing, Zhou, Wu, Wu, Wenfei, Zhang, Yiying |
| Conference Name | Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4265-0 |
| Keywords | Human Behavior, malware classification, Metrics, privacy, pubcrawl, Resiliency |
| Abstract | This paper calls for the attention to investigate real-world malwares in large scales by examining the largest real malware repository, VirusTotal. As a first step, we analyzed two fundamental characteristics of Windows executable malwares from VirusTotal. We designed offline and online tools for this analysis. Our results show that malwares appear in bursts and that distributions of malwares are highly skewed. |
| URL | http://doi.acm.org/10.1145/2967360.2967367 |
| DOI | 10.1145/2967360.2967367 |
| Citation Key | song_learning_2016 |