Name
Abstract | ||
|---|---|---|
Traditional machine learning techniques may suffer from evasion attack in which an attacker intends to have malicious samples to be misclassified as legitimate at test time by manipulating the samples. It is crucial to evaluate the security of a classifier during the development of a robust system against evasion attack. Current security evaluation for Support Vector Machine (SVM) is very time-consuming, which largely decreases its availability in applications with big data. In this paper, we propose a fast security evaluation of support vector machine against evasion attack. It calculates the security of an SVM by the average distance between a set of malicious samples and the hyperplane. Experimental results show strong correlation between the proposed security evaluation and the current one. Current security measure min-cost-mod runs 24,000 to 551,000 times longer than our proposed one on six datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BDS/HPSC/IDS18.2018.00062 | 2018 IEEE 4th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS) |
Keywords | DocType | ISBN |
Security Evaluation,Classifier's security,Evasion attack,Support Vector Machine | Conference | 978-1-5386-4400-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhimin He | 1 | 536 | 35.90 |
| Haozhen Situ | 2 | 43 | 10.96 |
| Y. Zhou | 3 | 163 | 37.69 |
| Jinhai Wang | 4 | 0 | 0.34 |
| Fei Zhang | 5 | 6 | 1.79 |
| Meikang Qiu | 6 | 3722 | 246.98 |