Name
Abstract | ||
|---|---|---|
The CAN-Bus is currently the most widely used vehicle bus network technology, but it is designed for needs of vehicle control system, having massive data and lacking of information security mechanisms and means. The Intrusion Detection System (IDS) based on machine learning is an efficient active information security defense method and suitable for massive data processing. We use a machine learning algorithm-Gradient Boosting Decision Tree (GBDT) in IDS for CAN-Bus and propose a new feature based on entropy as the feature construction of GBDT algorithm. In detection performance, the IDS based on GBDT has a high True Positive (TP) rate and a low False Positive (FP) rate. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-74176-5_25 | Lecture Notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering |
Keywords | DocType | Volume |
CAN-Bus,Information security,IDS,Machine learning,GBDT,Entropy,Detection performance | Conference | 221 |
ISSN | Citations | PageRank |
1867-8211 | 2 | 0.38 |
References | Authors | |
5 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daxin Tian | 1 | 204 | 32.49 |
| Yuzhou Li | 2 | 2 | 1.73 |
| Yunpeng Wang | 3 | 194 | 25.34 |
| Xuting Duan | 4 | 26 | 4.92 |
| Congyu Wang | 5 | 2 | 0.38 |
| WenYang Wang | 6 | 2 | 0.38 |
| Rong Hui | 7 | 2 | 0.38 |
| Peng Guo | 8 | 29 | 16.63 |