Name
Abstract | ||
|---|---|---|
In this paper, we compare the effectiveness of Hidden Markov Models (HMMs) with that of Profile Hidden Markov Models (PHMMs), where both are trained on sequences of API calls. We compare our results to static analysis using HMMs trained on sequences of opcodes, and show that dynamic analysis achieves significantly stronger results in many cases. Furthermore, in comparing our two dynamic analysis approaches, we find that using PHMMs consistently outperforms our technique based on HMMs. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/2875475.2875476 | IWSPA@CODASPY |
Keywords | DocType | Citations |
Malware, Hidden Markov Models, Profile Hidden Markov Models, Dynamic Analysis, Static Analysis | Journal | 4 |
PageRank | References | Authors |
0.44 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Swapna Vemparala | 1 | 4 | 0.44 |
| Fabio Di Troia | 2 | 41 | 3.12 |
| Corrado Aaron Visaggio | 3 | 619 | 45.84 |
| Thomas H. Austin | 4 | 307 | 15.96 |
| Mark Stamp | 5 | 513 | 33.32 |