Name
Abstract | ||
|---|---|---|
Due to the development of automated malware generation and obfuscation, traditional malware detection methods based on signature matching have limited effectiveness. Thus, a novel approach using visualisation and deep learning technology can play an important role in malware detection and classification. In this study, the authors extract sequences of API calls using dynamic analysis and then use ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1049/iet-ifs.2018.5268 | IET Information Security |
Keywords | Field | DocType |
application program interfaces,invasive software,learning (artificial intelligence),neural nets | Convolutional neural network,Computer science,Visualization,Theoretical computer science,Artificial intelligence,Deep learning,Malware,Obfuscation,Machine learning | Journal |
Volume | Issue | ISSN |
13 | 4 | 1751-8709 |
Citations | PageRank | References |
2 | 0.38 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tang Mingdong | 1 | 557 | 39.35 |
| Quan Qian | 2 | 13 | 4.54 |