Deep learning intrusion detection method and system based on Spark Internet of Vehicles combination

Abstract

The invention discloses a deep learning intrusion detection method and system based on Spark Internet of Vehicles combination. The method comprises the following steps: S1, establishing a Spark distributed cluster; S2, initializing the Spark distributed cluster, constructing a CNN-LSTM combined deep learning algorithm model, initializing parameters, and uploading the collected data to an HDFS; S3,reading the data from the HDFS for processing, inputting the data into a CNN-LSTM combined deep learning algorithm model, and recognizing the data; and S4, dividing the data into a plurality of RDDsfor batch training, and performing iteration for a preset number of times. The method provides a rapid and accurate detection method for intrusion detection of the Internet of Vehicles, ensures that the Internet of Vehicles can accurately and rapidly complete an intrusion detection task in a short time under the conditions that the computing power is limited, the application environment is complexand a large number of node networks exist, and provides a safe and reliable communication environment.

Description

technical field [0001] The present invention relates to the technical field of intrusion detection of Internet of Vehicles, in particular to an intrusion detection method and system based on deep learning of Spark Internet of Vehicles combination. Background technique [0002] In recent years, with the practical application of emerging technologies in the field of Internet of Vehicles, the Internet of Vehicles has developed more rapidly, and the communication between vehicles, roads, people, and clouds has become closer. Urban development plays a decisive role. With the improvement of communication capabilities, a large amount of network communication traffic also follows. However, due to the limited computing power, complex application environment, distributed multi-node and sensor network in the Internet of Vehicles, the security problems of the Internet of Vehicles are very prominent. How to ensure The security of the Internet of Vehicles and the acceleration of the appl...

AI Technical Summary

Problems solved by technology

[0004] However, the above algorithms cannot be directly applied in the actual environment of the Internet of Vehicles. First, the structure of the Internet of Vehicles is complex. Not only does the vehicle need to communicate with itself, but also the vehicle needs to interact with people, vehicles, roads, and the cloud; the second is network communication. There are many protocols and methods, not only Bluetooth, wireless, wired, but also mobile cellular network and LTE-V2X; third, the network topology changes rapidly, and vehicles are in the process of moving at high speed, so the network topology of the Internet of Vehicles is also based on the actual environment. ever changing

[0005] In view of the analysis of the above problems and the actual characteristics of the Internet of Vehicles, in order to solve the problem of the huge network traffic in the communication process of the Internet of Vehicles, which is difficult to detect quickly and effectively, and the accuracy of intrusion detection, it is necessary to propose a new intrusion detection solution for the Internet of Vehicles.

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