Method for detecting malicious attacks based on deep learning in traffic cyber physical system
US11777957B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 4, 2019 |
| Grant date | Oct 3, 2023 |
| Priority date | — |
| Expiry date | Jul 3, 2042 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04W12/128
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosed is a method for detection a malicious attack based on deep learning in a transportation cyber-physical system (TCPS), comprising: extracting original feature data of a malicious data flow and a normal data flow from a TCPS; cleaning and coding original feature data; selecting key features from the feature data; cleaning and coding the key features to establish a deep learning model; finally, inputing unknown behavior data to be identified into the deep learning model to identify whether the data is malicious data, thereby detecting a malicious attack. The present invention uses a deep learning method to extract and learn the behavior of a program in a TCPS, and detect the malicious attack according to the deep learning result, and effectively identify the malicious attack in the TCPS.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.