Method for detecting malicious attacks based on deep learning in traffic cyber physical system

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.