Name
Abstract | ||
|---|---|---|
Currently, malware are distributed or transmitted in a polymorphic form, smartly obfuscated with packing and encryption routines. This serves the purpose of hardening analysis or simply making it impossible. Researchers have mainly resorted to static analysis, dynamic analysis or a combination of both in attempting to find more adequate solutions to polymorphic malware problems. This paper presents a novel simple feature engineering approach in terms of extracting, analyzing and processing structural based features for efficient detection of polymorphic malware. Our experiments achieve a detection accuracy of 98.7% on a small dataset. |
Year | Venue | Field |
|---|---|---|
2017 | DASC/PiCom/DataCom/CyberSciTech | Data mining,Computer science,Static analysis,Feature extraction,Encryption,Feature engineering,Malware,Obfuscation |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emmanuel Masabo | 1 | 0 | 0.34 |
| Kyanda Swaib Kaawaase | 2 | 0 | 0.68 |
| Julianne Sansa-Otim | 3 | 0 | 1.69 |
| Damien Hanyurwimfura | 4 | 10 | 3.06 |