Name
Abstract | ||
|---|---|---|
Adversarial instances are malicious inputs designed to fool machine learning models. In particular, motivated and sophisticated attackers intentionally design adversarial instances to evade classifiers which have been trained to detect security violation, such as malware detection. While the existing approaches provide effective solutions in detecting and defending adversarial samples, they fail to detect them when they are encrypted. In this study, a novel framework is proposed which employs statistical test to detect adversarial instances, when data under analysis are encrypted. An experimental evaluation of our approach shows its practical feasibility in terms of computation cost. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-31511-5_5 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Privacy,Adversarial machine learning,Homomorphic encryption | Homomorphic encryption,Computer science,Adversarial machine learning,Encryption,Artificial intelligence,Malware,Statistical hypothesis testing,Machine learning,Adversarial system,Computation | Conference |
Volume | ISSN | Citations |
11738 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mina Sheikhalishahi | 1 | 6 | 4.66 |
| Majid Nateghizad | 2 | 3 | 2.43 |
| Fabio Martinelli | 3 | 751 | 82.27 |
| Zekeriya Erkin | 4 | 579 | 39.17 |
| Marco Loog | 5 | 1796 | 154.31 |