Name
Abstract | ||
|---|---|---|
This paper presents an anomaly detection method using a hybrid observer - which consists of a discrete state observer and a continuous state observer. We focus our attention on anomalies caused by intelligent attacks, which may bypass existing anomaly detection methods because neither the event sequence nor the observed residuals appear to be anomalous. Based on the relation between the continuous and discrete variables, we define three conflict types and give the conditions under which the detection of the anomalies is guaranteed. We call this method conflict-driven anomaly detection. The effectiveness of this method is demonstrated mathematically and illustrated on a Train-Gate (TG) system. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/acc.2018.8430988 | 2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC) |
Field | DocType | ISSN |
State observer,Anomaly detection,Computer science,Algorithm,Event sequence,Observer (quantum physics) | Conference | 0743-1619 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Wang | 1 | 0 | 0.34 |
| Farshad Harirchi | 2 | 19 | 4.62 |
| Dhananjay M. Anand | 3 | 10 | 4.81 |
| Chee Yee Tang | 4 | 0 | 0.34 |
| James R. Moyne | 5 | 209 | 72.77 |
| Dawn M. Tilbury | 6 | 900 | 123.02 |