Name
Abstract | ||
|---|---|---|
Along with the proliferation of IoT (Internet of Things) devices, cyberattacks towards them are on the rise. In this paper, aiming at efficient precaution and mitigation of emerging IoT cyberthreats, we present a multimodal study on applying machine learning methods to characterize malicious programs which target multiple IoT platforms. Experiments show that opcode sequences obtained from static analysis and API sequences obtained by dynamic analysis provide sufficient discriminant information such that IoT malware can be classified with near optimal accuracy. Automated and accelerated identification and mitigation of new IoT cyberthreats can be enabled based on the findings reported in this study. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1587/transinf.2018OFL0007 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
IoT security, IoT malware, malware analysis, malware classification | Computer vision,Computer science,Internet of Things,Human–computer interaction,Artificial intelligence,Cross-platform | Journal |
Volume | Issue | ISSN |
E102D | 9 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Ban | 1 | 102 | 25.58 |
| Ryoichi Isawa | 2 | 9 | 5.65 |
| Shin-Ying Huang | 3 | 0 | 2.03 |
| Katsunari Yoshioka | 4 | 147 | 22.92 |
| Daisuke Inoue | 5 | 67 | 17.51 |