Name
Title | ||
|---|---|---|
Evaluating Resilience of Encrypted Traffic Classification against Adversarial Evasion Attacks |
Abstract | ||
|---|---|---|
Machine learning and deep learning algorithms can be used to classify encrypted Internet traffic. Classification of encrypted traffic can become more challenging in the presence of adversarial attacks that target the learning algorithms. In this paper, we focus on investigating the effectiveness of different evasion attacks and see how resilient machine and deep learning algorithms are. Namely, we... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISCC53001.2021.9631407 | 2021 IEEE Symposium on Computers and Communications (ISCC) |
Keywords | DocType | ISBN |
Deep learning,Machine learning algorithms,Recurrent neural networks,Computational modeling,Classification algorithms,Internet,Cryptography | Conference | 978-1-6654-2744-9 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramy Maarouf | 1 | 0 | 0.34 |
| Danish Sattar | 2 | 10 | 3.58 |
| Ashraf Matrawy | 3 | 146 | 26.98 |