Name
Title | ||
|---|---|---|
Systematic Intrusion Detection Technique for an In-vehicle Network Based on Time-Series Feature Extraction |
Abstract | ||
|---|---|---|
In this paper, we propose a systematic intrusion detection algorithm based on time-series feature extraction for an in-vehicle network. Since packet-type valid data are transmitted inside an in-vehicle network periodically, illegal data due to unauthorized intrusion attack can be easily and uniformly detected by using periodical time-series feature of valid data, where recurrent neural network is a key tool to efficiently extract their time-series feature. In fact, through an evaluation using data acquired from actual vehicles, we show that the proposed method can detect typical intrusion attack patterns such as data modification attack and injection attack. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ISMVL.2018.00018 | 2018 IEEE 48th International Symposium on Multiple-Valued Logic (ISMVL) |
Keywords | Field | DocType |
car security,controller area network,intrusion detection system,deep learning,recurrent neural network | CAN bus,Data mining,Attack patterns,Intrusion,Computer science,Vehicle networks,Recurrent neural network,Feature extraction,Electronic engineering,Artificial intelligence,Deep learning,Intrusion detection system | Conference |
ISSN | ISBN | Citations |
0195-623X | 978-1-5386-4465-2 | 1 |
PageRank | References | Authors |
0.38 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroki Suda | 1 | 1 | 0.38 |
| Masanori Natsui | 2 | 80 | 15.10 |
| Takahiro Hanyu | 3 | 441 | 78.58 |