Name
Title | ||
|---|---|---|
Interpretable deep learning method for attack detection based on spatial domain attention |
Abstract | ||
|---|---|---|
Deep learning methods can directly extract effective features from original data. However, this type of model is complex and considered to be a “black box”, which leads to low interpretability of the models. Since the results of attack detection are significant to cybersecurity, every decision should be supported with convincing reasons. Hence, the problem of interpretability has become a bottlene... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISCC53001.2021.9631532 | 2021 IEEE Symposium on Computers and Communications (ISCC) |
Keywords | DocType | ISBN |
Deep learning,Computers,Visualization,Computational modeling,Semantics,Telecommunication traffic,Network security | Conference | 978-1-6654-2744-9 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongyu Liu | 1 | 1 | 0.70 |
| Bo Lang | 2 | 341 | 22.09 |
| Shaojie Chen | 3 | 0 | 0.34 |
| Mengyang Yuan | 4 | 0 | 0.34 |